A survey on various security protocols of edge computing

Abstract

Edge computing has emerged as a transformative data processing method by decentralizing computations and bringing them toward the data source, significantly reducing latency and enhancing response times. However, this shift introduces unique security challenges, especially within the detection and prevention of cyberattacks. This paper gives a comprehensive evaluation of the edge security landscape in peripheral computing, with specialized expertise in identifying and mitigating various types of attacks. We explore the challenges associated with detecting and preventing attacks in edge computing environments, acknowledging the limitations of existing approaches. One of the very interesting novelties that we include in this survey article is, that we designed a Web application that runs on an edge network and simulates SQL injection attacks-a common threat in edge computing. Through this simulation, we examined every one of the cleanup strategies used to discover and prevent such attacks using input sanitization techniques, ensuring that the malicious SQL code turned neutralized. Our studies contribute to deeper know-how of the security landscape in edge computing by providing meaningful insights into the effectiveness of multiple prevention strategies.

Introduction

In the era of digitalization [1], cloud computing has become a revolutionary force due to the exponential growth in demand for computing resources. Through the internet, this paradigm makes a wide range of virtualized computing services from databases and storage to software applications available to both individuals and enterprises. Cloud computing [2] promises scalability, flexibility, and cost-efficiency previously unthinkable with traditional on-premises IT setups by moving the load of infrastructure upkeep to service providers. Without being constrained by physical hardware, organizations may exploit advanced IT capabilities, pay for only what they use, and grow resources dynamically.

The advancement of cloud computing has underscored some drawbacks, particularly concerning latency and bandwidth efficiency [3]. The physical separation between users and centralized cloud data centers [4] can cause delays in data transmission, affecting real-time applications like IoT devices, autonomous systems, and immersive technologies. Additionally, transferring large data volumes to and from centralized servers can strain network bandwidth, resulting in congestion and higher expenses, especially in settings with restricted network capacity [5]. To address these challenges, the concept of edge computing came into existence. We represent a thorough diagrammatic representation of how edge, cloud and fog computing are connected in Fig. 1.Fig. 1 illustrates the interactions between devices, edge nodes, fog nodes, and cloud data centers in a computing ecosystem. It highlights how data flows through different layers to achieve optimal processing, storage, and analysis. Thereby, Fig. 1 illustrates the visual benefits of edge computing over cloud and fog computing for the readers to understand effectively.

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Fig. 1

Edge, cloud and fog computing framework

Concept of edge computing

Edge computing [6] is a distributed computing paradigm that positions data processing and storage closer to the sources of data generation, such as IoT devices, sensors, and local servers [7]. Unlike the traditional cloud computing model, which centralizes data processing in large data centers, edge computing processes data at the “edge” of the network, near the data source.

This model of distributed computing minimizes the requirement for continuous communication with centralized cloud servers by utilizing nearby computer resources to handle data locally [8]. For applications like industrial IoT [9], autonomous vehicles, and augmented reality that demand instantaneous data processing and real-time decision-making, edge computing is especially advantageous. A synergistic ecosystem of centralized cloud services and localized edge processing is created when cloud computing and edge computing work together to provide effective, scalable, and responsive answers to contemporary computing problems. We describe various key components and motivations for using edge computing in the current trend.

Please refer to Fig. 2 for the market size trends in edge computing. Fig. 2 represents the projected growth of the edge computing market from 2021 to 2027. It provides a visual depiction that in the year 2021, the growth projection was 3.5 billion dollars whereas in the year 2027, it will be 43.4 billion dollars. Therefore, we can see that there will be an exponential increase in the market of edge computing.

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Fig. 2

Projected growth of edge computing

Key components of edge computing

Edge computing is made possible by a network of interconnected components, each with a critical role in enabling efficient, low-latency data handling and real-time decision-making [10]. Understanding these components is critical for understanding how edge computing increases operational efficiency, and responsiveness. At the same time, edge computing supports new applications across multiple industries. We describe the key components of an edge computing framework [11] in the following subsections.

Fig. 3 represents the flow and interaction of components within an edge computing system. It outlines how data is collected, processed, and analyzed at various stages from edge devices to edge computing software, with security measures integrated throughout. We discuss every component of the edge network below.

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Fig. 3

Edge Computing Components

Edge Devices: Edge devices [12] are the primary endpoints in edge computing, where data originates and is first collected [13]. These devices come in a variety of forms, including industrial machines with sensors, cellphones, and Internet of Things sensors integrated into the infrastructure. Gathering unprocessed data from the real world like temperature readings, motion detection, video streams, or operational metrics [14], is their main duty. The capacity of edge devices to engage immediately with their surroundings and sense events and changes instantly sets them apart. They serve as the initial point of contact in the chain of data processing, starting the information flow that powers further analysis and decision-making. IoT sensors, smartphones, etc., are some of the examples.

Edge Nodes: Edge nodes [15] are intermediary devices that are positioned closer to edge devices in a localized network environment. They are essential in processing and combining data obtained from various edge devices before sending it via the data pipeline [16].

Edge nodes improve operational efficiency by removing unnecessary information from the stream, standardizing data formats [17], and carrying out simple calculations locally. By reducing the amount of data that needs to be sent to centralized cloud servers or other edge components, this preliminary data processing optimizes bandwidth utilization and minimizes delay. In addition, edge nodes facilitate local decision-making by acting as standalone data processing units, responding to events or triggers in real time using predetermined algorithms or rules. Local servers [18] and routers work as edge nodes.

Edge Data Centers: Edge data centers [19] are localized facilities that provide additional computing power and storage capacity near edge devices and nodes [20]. Unlike standard cloud data centers [21], which are often centralized and service wide geographic areas, edge data centers are intentionally spread to support specific locations or regions. For more complex computational processes and applications that demand more processing power than edge nodes alone can provide, they provide a reliable infrastructure.

By shortening the path between data collection and processing, edge data centers minimize latency [22] and facilitate quicker real-time data analysis. This results in increased responsiveness. Additionally, they are essential for managing and storing data, which guarantees local data availability and accessibility for vital edge-operating applications and services.

Edge Computing Software: Edge computing software includes a collection of frameworks, platforms, and middle ware [23] intended to efficiently oversee and coordinate edge computing activities [24]. These software solutions are designed to support different facets of edge computing, such as processing data, deploying applications, managing security [25], and integrating with centralized cloud services. They are crucial for optimizing how data moves and how communication is handled among edge devices, nodes, and central data centers. This ensures smooth functioning and performance across distributed networks. By using edge computing software, organizations can effectively deploy and oversee edge applications, tailoring them to specific industry needs and use cases. This enhances the overall scalability and flexibility of their systems.

Edge Gateways: Edge gateways [26] function as intermediate devices that link edge devices with broader network infrastructures or centralized cloud services [27]. They facilitate seamless connectivity and data transmission by managing communication protocols, aggregating data, and overseeing security functions at the edge. Integral to integrating diverse edge devices and nodes into a unified network environment, edge gateways ensure efficient interoperability and smooth data exchange. They enforce security measures such as data encryption, access control, and authentication to uphold data integrity and confidentiality during transmission between edge components and external networks or cloud-based services [28].

Edge Security: Edge security involves implementing protective measures at the network’s periphery to defend data, devices, and networks against cybersecurity [29] threats and unauthorized access [30]. These security measures are specifically adapted for edge computing environments, where data processing and storage occur nearer to the data source.

Edge security solutions encompass encryption methods, access controls, authentication protocols, and intrusion detection systems. Their purpose is to protect sensitive data and guarantee the confidentiality, integrity, and availability of information. Securing data at the edge helps organizations minimize risks linked to data breaches [31], unauthorized entry, and malicious assaults, thereby promoting confidence and dependability in edge computing implementations [32].

Every element of edge computing is essential to facilitating decentralized data processing [33], improving system performance, and opening up new possibilities for creative applications in a range of sectors. Through the utilization of edge devices, nodes, data centers, computing software, analytics capabilities, gateways, and strong security measures, enterprises can fully leverage edge computing’s potential to propel digital transformation [34], enhance decision-making, and provide customized experiences in the dynamic digital environment.

To effectively build and deploy edge computing solutions that meet specific use cases and business needs, one must have a thorough understanding of the functionality and interconnections of various components [35].

We illustrate the advantages of edge computing in the following subsection (see sec. 1.1.2).

Advantages of edge computing

In this section, we describe the advantages of edge computing over cloud and fog computing.

Low Latency and Real-Time Processing: Edge computing reduces latency by processing data closer to where it is generated, typically at the edge of the network [36]. This proximity minimizes the time it takes for data to travel between devices and centralized cloud servers, ensuring faster response times for critical applications. Real-time processing capabilities enable immediate actions based on up-to-date data, which is essential for applications like autonomous vehicles, industrial automation, and real-time analytics [37].

Strengthened Security and Privacy: Edge computing enhances security by keeping sensitive data localized and reducing exposure to external threats. Data can be encrypted at the edge before transmission to the cloud or other devices, ensuring confidentiality and integrity [38]. Localized processing also reduces the risk of data breaches during data transmission over public networks, addressing concerns related to data privacy compliance and regulatory requirements [39].

Efficient Bandwidth Management: Edge computing optimizes bandwidth usage by reducing the amount of data that needs to be transmitted to centralized cloud servers. Edge nodes [40] process and filter data locally, sending only relevant insights or summarized data to the cloud. This approach minimizes network congestion, lowers data transmission costs, and conserves bandwidth, especially in environments with limited network capacity or high data volumes [41].

Scalability and Adaptability: Edge computing [42] offers scalable solutions tailored to specific application needs and geographical locations. Organizations can deploy additional edge nodes or data centers as needed to handle increasing data volumes or computational demands [43]. This flexibility supports dynamic workload distribution and resource allocation across distributed networks, accommodating fluctuating demands and optimizing operational efficiency.

Improved Reliability and Continuity: Edge computing enhances reliability by enabling applications to operate autonomously even when connectivity to centralized cloud services is disrupted [44]. Edge nodes and devices continue to function and process data locally, ensuring uninterrupted operations in remote or challenging environments with unreliable network connectivity [45]. This capability is crucial for mission-critical applications in industries such as manufacturing, transportation, and utilities.

Please refer to the table 1 for a comparison of edge, cloud, and fog computing in different aspects. To make it easy for the viewers to understand the literature in this section, we represent the comparison between edge, fog, and cloud computing in table 1.

Table 1. Comparison of Edge, Cloud, and Fog Computing

Aspect	Edge computing	Cloud computing	Fog computing
Architecture	Decentralized	Centralized	Distributed
Latency	1-10ms	50-200ms	10-50ms
Bandwidth	Reduces by 90%	5-25 Mbps per stream	Reduces by 70%
Security	Local data processing	Data in transit vulnerable	Distributed encryption
Use Cases	Real-time monitoring: 3 ms	Data analytics: 150 ms	Connected vehicles: 20 ms

In the following section(refer sec. 1.2, we discuss why we shifted to edge computing when the world was busy improvising and using cloud infrastructure.

Need of edge computing

The primary question now arises when cloud computing and fog computing were widely used by the industry, why did we need to implement edge computing? Well, the answer lies in the fact that edge computing is needed primarily because of the increasing demand for real-time data processing and low-latency applications that traditional cloud computing [46] struggles to support adequately.

The critical reason for edge computing lies in its ability to process data closer to where it is generated, thereby reducing the time it takes for data to travel between the source and the processing unit. This proximity is crucial for applications such as autonomous vehicles [47], industrial automation, and real-time analytics, where even milliseconds of delay can impact performance, safety, and operational efficiency [48]. By decentralizing computation to the edge of the network, edge computing addresses these latency challenges effectively.

Moreover, edge computing enhances data privacy and security by keeping sensitive information local, minimizing the exposure of data during transmission over public or private networks [49]. This local processing capability also improves reliability, as applications can continue to function autonomously even when network connectivity to centralized cloud services is disrupted. In essence, the fundamental need for edge computing arises from its ability to provide faster response times, enhanced security, and improved reliability in environments requiring immediate data processing and efficient network utilization.

Edge computing is fundamentally different and more efficient than cloud and fog computing because of its approach to data processing location and latency reduction [50]. Edge computing processes data locally, immediately at the source, or close by on edge devices and local servers, in contrast to cloud computing, which centralizes data processing in faraway data centers and thus introduces substantial delay. Because of the significant reduction in latency caused by this near proximity, edge computing is perfect for real-time applications like smart cities [51], industrial automation, and autonomous cars. Additionally, edge computing minimizes data transmission to central data centers, optimizing bandwidth use while lowering expenses and easing network congestion [52]. Fig. 4 illustrates the adoption rates of three key computing paradigms-cloud computing, edge computing, and fog computing-over the period from 2021 to 2027. This graph clearly depicts that over the year the adoption rate of edge computing has increased significantly. In the year 2021, edge computing had an adoption percentage of 20%, whereas in the year 2023, the adoption rate was around 72%. It is estimated that by the year 2027, the adoption rate would be almost 80%.

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Fig. 4

Adoption rate of cloud,edge and fog computing since 2021

When compared to cloud computing, fog computing [53] reduces latency by distributing processing among intermediary nodes such as gateways and routers; yet, it still falls short of edge computing’s extremely low latency and instantaneous responsiveness. Furthermore, by keeping sensitive data local and lowering the chance of exposure during transmission, edge computing improves data security and privacy [54]. Since apps may continue to run without a network connection, this local processing also guarantees increased resilience and reliability. All things considered, edge computing is a more efficient, secure, and high-performing option than cloud computing or fog computing since it can handle data in real time and with minimal latency [55].

Security in edge computing

Edge computing offers significant security advantages over traditional centralized cloud systems. Let’s break down how it strengthens our digital defenses:

Localized data handling: Edge computing [56] has revolutionized data handling [57] by bringing processing capabilities closer to the source, fundamentally altering the landscape of data security [58] and efficiency. This localized approach significantly reduces the need for long-distance data transfers, a key factor in minimizing interception risks and protecting sensitive information. In real-world applications, this concept is particularly crucial for industries like healthcare [59] and autonomous vehicles [60].

Consider a hospital using IoT devices to monitor patient vital signs. With edge computing, these devices can process and analyze data locally, only sending critical information to central servers. This not only ensures patient privacy but also allows for immediate response to emergencies without relying on distant cloud servers. For instance, a heart rate monitor could detect an irregularity and trigger an immediate alert to nearby medical staff, all processed at the edge without the delay of cloud communication [61].

Rapid threat response: Rapid threat response [62] is a pivotal advantage of edge computing, especially in an era where cyber threats [63] are increasingly sophisticated and quick to propagate. The core of this benefit lies in the reduced latency inherent in edge computing, which enables immediate detection and response to security issues. Unlike traditional cloud computing [64], which often involves sending data to a centralized server for processing, edge computing processes data locally, close to its source. This proximity significantly diminishes the time lag between data generation, processing, and response, allowing for almost instantaneous threat detection and mitigation.

To illustrate the pivotal advantage of rapid threat response in edge computing, Fig. 5. provides a visual comparison of latency between edge computing, fog computing, and traditional cloud computing [65]. As discussed, edge computing’s localized data processing near the data source minimizes latency, enabling almost instantaneous detection and mitigation of security threats. This graph complements the discussion by demonstrating how edge computing’s reduced latency facilitates quicker response times compared to the intermediate processing of fog computing and the centralized processing approach of traditional cloud computing.

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Fig. 5

Comparison of Latency between Edge, Fog and Cloud Computing

In practical terms, consider the example of a smart factory employing edge computing to monitor its machinery and production lines [66]. Sensors embedded in the equipment constantly generate data on performance metrics such as temperature, vibration, and operational speed. With edge computing, this data is processed in real time by local edge devices, enabling the system to quickly identify anomalies [67] that may indicate a malfunction or security breach. If a cyber attack [68] attempts to disrupt the production line, the edge computing system can detect the unusual data patterns or unauthorized access attempts and trigger immediate countermeasures. This rapid response can prevent the attack from causing significant damage or spreading further within the network.

Enhanced Privacy Compliance: Enhanced privacy compliance [69] in edge computing represents a significant advancement in how data privacy and regulatory requirements are managed. This technological approach allows data to be processed locally, near its source, which helps minimize the risks associated with long-distance data transmission and central cloud storage.

One of the primary advantages of edge computing is its ability to handle sensitive data at the local level. This localized processing ensures that data remains closer to its origin, reducing the chances of interception or unauthorized access during transit. For instance, in healthcare applications [70] where patient data is highly sensitive and protected under regulations like the Health Insurance Portability and Accountability Act (HIPAA) [71], edge computing can be used to process and analyze data directly on medical devices or local servers within a hospital. This localized handling means that patient information does not need to travel to a centralized cloud, thus minimizing exposure to potential breaches and ensuring compliance with strict privacy laws.

Resilient Distributed Architecture [72]: Edge computing’s decentralized nature significantly enhances its resilience against cyber-attacks compared to traditional centralized cloud systems [73]. Unlike centralized systems, where a single point of failure can jeopardize the entire network, edge computing distributes data and processing tasks across multiple devices and locations. This distribution makes it considerably harder for attackers to compromise the entire system, as they would need to breach numerous points simultaneously, which is far more complex and resource-intensive.

The network diagram Fig. 6 visually illustrates the architectural differences between centralized cloud systems and decentralized edge computing, highlighting how edge computing’s distributed nature enhances resilience against cyber-attacks. As discussed, edge computing disperses data and processing across multiple devices and locations, mitigating the risk of a single point of failure that could compromise the entire network. Fig. 6 complements this discussion by visually demonstrating how distributing data and processing tasks across edge devices enhances the system’s overall security and resilience against cyber threats.

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Fig. 6

A network diagram showing the difference in architecture between centralized cloud and decentralized edge computing

One of the key benefits of this distributed architecture [74] is its ability to isolate and contain attacks. For instance, if an edge device at a specific location is compromised, the attack is likely to be contained to that particular node, preventing it from spreading to the rest of the network. This containment is crucial in mitigating the damage and quickly addressing the breach without affecting the entire system. For example, in a smart city [75] scenario, where various IoT devices manage traffic lights, environmental sensors, and public safety systems, a breach in one sensor can be isolated, ensuring that other critical infrastructure components continue to operate smoothly.

Adaptive Security Measures: Edge computing introduces adaptive security measures [76] that significantly enhance data protection and threat mitigation in various environments. Unlike traditional centralized systems, edge devices can implement security policies [77] tailored to their specific local contexts. This adaptability allows for more nuanced and effective security strategies, capable of dynamically responding to changing conditions and emerging threats.

One key advantage of adaptive security in edge computing is its ability to customize security protocols [78] based on real-time data and environmental factors. For instance, in a smart city deployment [79], edge devices installed in different locations can adjust their security measures according to local traffic patterns, weather conditions, and user activities. This localized approach ensures that security measures are always relevant and optimized for the immediate environment, thereby reducing the risk of unauthorized access and data breaches.

Once we describe the security measures in edge computing, we shed light on the main focus and novelty of this research work in the following subsections(refer sec. 1.4, and sec. 1.5)

Motivation

This study is motivated by rapid growth in IoT devices [80] and their applications and widespread in the era of Edge computing. Processing the data and sending a response to the device at the device location is a significant challenge because the system must handle a variety of complex tasks in real-time while ensuring low latency and high reliability. We design this survey article based on our research and experience with the security aspects of edge computing. In this paper, we try to explain how security works in edge computing and delve into the various mechanisms that enhance its security framework. We will explore how edge computing brings data processing closer to the source, thereby reducing the risks associated with data transmission over long distances.

Motivation 1: IoT Device Proliferation. The rapid increase in Internet of Things (IoT) devices, from smart home gadgets to industrial sensors, has created an urgent need for robust and scalable security solutions [81]. Each of these devices generates data that must be processed securely, often in real-time, to ensure the integrity and confidentiality of information. Edge computing is uniquely positioned to handle these tasks by processing data closer to the source, thus reducing potential vulnerabilities associated with data transmission over the internet.

Motivation 2: Real-Time Data Processing and Decision Making. Many applications, such as autonomous vehicles [82], healthcare monitoring systems, and industrial automation [83], require real-time data processing and decision-making. The ability of edge computing to process data locally and provide immediate responses is crucial for the safe and efficient operation of these systems. Security at the edge is vital to prevent malicious interference that could lead to catastrophic outcomes in these real-time applications.

Motivation 3: Smart Cities and Public Safety. The development of smart cities involves integrating various IoT devices and edge computing systems to improve public services [84], traffic management [85], and emergency response [86]. Securing these interconnected systems is crucial to protecting citizens’ privacy and ensuring public safety. The complexity and scale of security requirements in smart cities drive the need for focused research on edge computing security.

Motivation 4: Securing Critical Infrastructure. Critical infrastructures, such as power grids [87], water treatment facilities [88], and transportation systems [89], increasingly rely on edge computing for real-time monitoring and control. Ensuring the security of these systems is paramount, as breaches can lead to significant societal and economic disruptions. The role of edge computing in securing these infrastructures provides strong motivation for research into advanced security measures.

Motivation 5: Reduction of Latency in Security Threat Detection. Traditional cloud-based security models [90] can suffer from significant latency, delaying the detection and mitigation of security threats. Edge computing’s ability to provide low-latency processing means that threats can be identified and neutralized more quickly, reducing the window of opportunity for attackers. This aspect of edge computing’s security capabilities is particularly motivating for research, as it can lead to the development of faster and more effective security responses.

Therefore, it is high time, we must be aware of security measures and security threats in an edge computing network. Therefore. this research intends to provide a deep insight into the security threats, detection of those threats, and prevention of attacks in an edge computing network.

Novelty of the research

Edge computing represents a paradigm shift from centralized cloud architectures, bringing data processing closer to its source. This transition offers benefits like reduced latency and improved response times, but it also introduces unique security challenges that vary based on the nature and deployment context of edge devices [91]. This research aims to highlight these distinct security concerns and present tailored solutions for different categories of edge devices, offering a more nuanced perspective than existing literature that often generalizes security measures [92].

The diverse landscape of edge devices, ranging from simple sensors to complex autonomous vehicles [93], necessitates a differentiated approach to security. This study distinguishes between traditional edge devices with limited resources and intelligent edge devices with advanced processing capabilities, exploring their distinct security needs and proposing specific techniques suited to each type. This approach addresses the heterogeneous nature of edge computing environments more effectively than a one-size-fits-all strategy.

Further to illustrate the security challenges of the edge computing area, we will present the development of a Web application able to simulate and handle one of the most popular threats in such environments: SQL injection attacks. Using this application we can show how to detect or prevent SQL injection attacks through input sanitization [94]. This is an important practical example of how it is fundamental to embed robust security measures directly in edge computing systems, providing a concrete illustration of many of the concepts that were addressed in this study.

A significant novelty of this research is the evaluation of simulators for testing edge computing solutions. These tools are crucial for modeling complex scenarios and predicting system behavior under various conditions. The paper reviews current edge computing simulators, discusses their features and limitations, and guides their effective use. This practical focus bridges the gap between theoretical insights and real-world application, offering valuable resources for both researchers and practitioners.

The novelty of this work lies in its comprehensive, tailored approach to edge computing security. By differentiating between device types and examining appropriate security solutions for each, the research provides a more precise framework for securing edge environments. The inclusion of insights into simulation techniques enhances the practical applicability of the findings. This multifaceted approach not only contributes to academic discourse but also addresses practical challenges in implementing secure edge computing solutions across various industries.

Furthermore, this study expands on earlier research by citing previous studies while also conducting our experiments to ensure the consistency and dependability of those methods that have already been implemented. By performing these trials, we ensured that the recommended solutions produced consistent results and found any inconsistencies. This combined approach of referencing prior research while also undertaking independent validation increases the legitimacy of our findings and lays a solid platform for future advances in edge computing security.

Literature survey

Security Aspects in Edge Computing:  Edge computing represents a paradigm shift in data processing, bringing computational capabilities closer to data sources. While offering numerous benefits like reduced latency and enhanced efficiency, edge computing introduces unique security challenges. This section explores how various aspects of security, including AI-driven solutions, cryptographic techniques [95], blockchain applications [96], and defense against different types of attacks, are applied in edge computing environments.

AI-driven security solutions in edge computing

Artificial Intelligence (AI) [97] is revolutionizing security in edge computing by leveraging advanced algorithms and machine learning techniques [98] to enhance threat detection, data privacy, and adaptive security measures. Below is an expanded overview of how AI contributes to each aspect of security in edge computing:

1. Threat Detection and Prevention: AI models are increasingly deployed at the network edge to detect and prevent cyber threats in real-time. These models use machine learning [99] algorithms to analyze vast amounts of data generated by edge devices. By continuously learning from patterns and behaviors, AI can identify known threat signatures and even predict new, previously unseen threats.

   For instance, AI can detect malware [100] communication protocols and block them before they cause damage. Real-time analysis allows for immediate action, reducing the window of opportunity for attackers. Additionally, AI can recognize sophisticated attack patterns, such as those used in advanced persistent threats [101] (APTs), which traditional security measures might miss.

2. Anomaly Detection: Anomaly detection is a crucial aspect of AI-driven security in edge computing. AI algorithms [102] establish baselines of normal behavior for each device and network segment. By continuously monitoring and analyzing data, AI can identify deviations from these baselines, flagging [103] them as potential security incidents.

   For example, if an IoT device suddenly starts transmitting data at an unusual time or volume, AI can flag this activity as suspicious. This early detection of anomalies allows for quick investigation and response, minimizing the impact of potential security breaches. Anomaly detection also helps in identifying zero-day exploits, which are previously unknown vulnerabilities [104] exploited by attackers.

3. Enhanced Data Privacy: AI-driven edge security [105] significantly enhances data privacy by processing sensitive information locally, reducing the need for data transmission to centralized servers. This localized data processing ensures that sensitive information, such as personal health records or financial transactions, is protected from interception during transmission.

   AI algorithms can also apply advanced encryption techniques to data at the edge, ensuring compliance with data protection regulations like the General Data Protection Regulation (GDPR) [106]. Additionally, AI can manage data retention policies, ensuring that only necessary data is stored and that it is retained for the appropriate duration, further minimizing privacy risks.

4. Adaptive Security Policies: One of the standout features of AI in edge computing is its ability to implement adaptive security policies. These policies dynamically adjust based on real-time data analysis and threat intelligence [107]. AI systems can assess the current risk landscape and modify security measures accordingly.

   For instance, during a detected increase in cyber threat activity [108], AI can tighten security controls, such as increasing the sensitivity of intrusion detection systems or applying more stringent access controls. Conversely, during periods of low-threat activity, AI can optimize system performance by reducing the overhead of security processes. This dynamic adjustment ensures optimal protection without compromising system efficiency.

5. Fraud Detection: Fraud detection [109] in edge computing is enhanced by AI’s ability to analyze transactional and behavioral data in real-time. AI models, such as neural networks and anomaly detection algorithms, are trained on extensive datasets to distinguish between legitimate and fraudulent activities.

   For example, in financial services, AI can monitor transactions for patterns indicative of fraud, such as unusual spending behavior or multiple transactions in quick succession. In industrial settings, AI can detect fraudulent manipulation [110] of sensor data or unauthorized access to control systems. By providing real-time alerts, AI enables swift action to prevent financial losses and protect critical infrastructure.

6. Advancements in AI-Driven Security for Edge Computing: AI’s function in edge computing security keeps evolving, substantially boosting the performance and adaptability of protection mechanisms. One of the biggest advances in recent years is using deep learning algorithms [111]. These AI models process and research from widespread datasets, letting them understand subtle patterns that differentiate valid from malicious behaviors. This capability is important for detecting modern-day attacks that conventional, rule-based systems [112] often neglect. For instance, deep neural networks can come to be aware of anomalies at a granular level, detecting minor deviations in network visitors or device conduct that would signal a rising risk.

   Behavioral analysis [113] is any other area wherein AI has been confirmed exceedingly powerful. By constructing behavioral profiles of users and devices, AI can distinguish between normal and ordinary movements. These profiles are continuously updated, permitting the system to discover deviations in real-time. If a tool all of sudden behaves outside its everyday pattern-like getting access to unusual statistics or beginning surprising connections-the AI can reply without delay, triggering indicators or blockading sports. This adaptability minimizes false positives at the same time as ensuring that the right threats are caught early.

   Moreover, AI systems are now moving beyond simple reactive defenses by using predictive analytics [114]. By analyzing past data and identifying patterns, AI can anticipate potential threats before they become serious. For example, if an AI system notices a series of small, unusual activities that don’t pose an immediate threat but hint at someone testing the defenses, it can proactively adjust security measures to counteract the potential risk. This predictive capability is crucial for preventing superior chronic threats [115], in which attackers spend extended durations collecting data earlier than launching a full-scale assault.

   AI is likewise improving the encryption and records obfuscation processes at the brink. By studying the sensitivity and kind of records in real time, AI can decide the most excellent stage of encryption wished, dynamically adjusting it based on the perceived danger degree. This flexibility guarantees that safety features are usually in track with the modern-day risk surroundings without overburdening side devices, which frequently have constrained processing strength. The integration of AI with modern cryptographic strategies, like homomorphic encryption [116], allows stable computations on encrypted records at once at the threshold, decreasing the want to transmit touchy statistics to centralized servers.

   In terms of scalability, AI offers a significant advantage in managing security across distributed systems. Unlike conventional methods [117] that depend on centralized data centers, AI can operate directly at the edge of the network, allowing it to secure multiple devices simultaneously. This decentralized approach reduces latency, enabling quicker detection and response to potential threats. AI’s ability to handle security at the edge ensures that as networks expand, security measures remain effective and responsive without relying solely on a central point of control.

   Continuous skill improvement is the cornerstone of an AI-driven protection response [118]. Threshold AI fashions are designed to analyze and therefore order from every interaction. This functionality considers that the brand new statistical AI works, the more intelligent and powerful it becomes at detecting threats. For instance, whilst the AI model encounters new malware, it may distribute this understanding throughout the network, ensuring that every single related tool is prepared to address comparable threats Such intelligence a this postpone enhances proactive protection measures and coping mechanisms.

   Additionally, integrating AI into incident response streamlines security operations. Once an abnormality is detected [119], AI systems can routinely initiate reaction moves, alongside setting apart compromised devices or restoring them to a stable tool Simply through the usage of processing these duties without fee, AI reduces the weight on human operators, letting them awareness on greater complex protection stressful situations. This automation accelerates reaction instances and decreases capacity harm from attacks, particularly in situations in which a fast response is needed.

   The position of AI in fraud detection has additionally progressed, with side devices now in a position to analyze network patterns in actual time to stumble on anomalies Machine analyzing models [120] skilled on a substantial sort of records can pick out out subtle fraud signs fraud, which includes too many connections or uncommon places Even a small delay in detecting fraud can result in large monetary losses. By processing this facts regionally at the edges, AI structures hold low latency, ensuring that fraudulent interest is detected and addressed almost without delay.

   As AI-driven solutions continue to evolve, their ability to balance high-level security [121] with the practical constraints of edge computing becomes increasingly refined. These advancements offer a robust framework for the future, where AI’s adaptability, predictive power, and decentralized nature play pivotal roles in safeguarding data and devices in an ever-expanding network landscape.

Cryptographic techniques in edge computing

Cryptographic techniques [122] are crucial for ensuring the security and privacy of data in edge computing environments. These techniques safeguard sensitive information as it moves between devices and edge servers, enabling secure data processing and storage. Here’s a more detailed exploration of various cryptographic methods used in edge computing [123]:

Attribute-based encryption (ABE)

Attribute-Based Encryption [124] (ABE) is a type of public-key encryption where the secret key of a user and the cipher-text [125] are dependent upon attributes (e.g., user’s roles or other properties). In ABE, a user’s decryption key is associated with a set of attributes, and the cipher-text specifies an access policy over these attributes.

1. Key Policy Attribute-Based Encryption (KP-ABE):

   In KP-ABE, the ciphertext is associated with a set of attributes, and the user’s secret key is associated with an access policy. A user can decrypt a ciphertext [126] if and only if the attributes of the ciphertext satisfy the access policy in the user’s key.

   • Encryption Equation:$$\begin{array}{c}\hfill E=(C,{\{C_i=g^{r{A}_i}\}}_{iϵI})\end{array}$$ where C is the ciphertext, r is a random value, and $A_i$ are the attributes.

2. Cipher-text-Policy Attribute-Based Encryption (CP-ABE): In CP-ABE, the ciphertext [127] is encrypted with an access policy, and the user’s secret key is associated with a set of attributes. Decryption is possible only if the user’s attributes satisfy the access policy specified in the ciphertext.

   • Encryption Equation:$$\begin{array}{c}\hfill E=(C=M·{(g,g)}^{\alpha s}{\{C_i=g^{s{A}_i}\}}_{iϵI})\end{array}$$ where M is the message, $\alpha$ and s are random values, and $A_i$ are the attributes.

Searchable encryption

Searchable Encryption [128] (SE) enables search operations on encrypted data without revealing the plain-text. This is particularly useful in scenarios where data privacy is critical, but search functionality is required.

1. Searchable Symmetric Encryption (SSE): In SSE, both the data and the search queries are encrypted using symmetric keys. The server can perform search operations on the encrypted data without knowing the actual content.

   • Encryption Equation:$$\begin{array}{c}\hfill E_k(w)=\{c_i=En{c}_k(m_i)|iϵ[1,n]\}\end{array}$$ where w is the keyword, k is the symmetric key, and $m_i$ are the messages.

2. Searchable Asymmetric Encryption (SAE): In SAE, public-key encryption [129] is used, allowing anyone to encrypt the data, but only the holder of the private key can perform search operations.

   • Encryption Equation:$$\begin{array}{c}\hfill E_{\mathit{pk}}(w)=\{c_i=En{c}_{\mathit{pk}}(m_i)|iϵ[1,n]\}\end{array}$$ where w is the keyword, pk is the public key, and $m_i$ are the messages.

Homomorphic encryption

Homomorphic Encryption [116] allows computations to be performed on encrypted data without decrypting it. This property is beneficial for preserving privacy while enabling data analysis and processing in edge computing.

1. Partially Homomorphic Encryption (PHE): PHE supports specific types of operations, such as addition or multiplication, on encrypted data.

   • Example: RSA Homomorphic Addition$$\begin{array}{c}\hfill Enc(m_1)·Enc(m_2)=Enc(m_1+m_2)\end{array}$$ where $m_1$ and $m_2$ are messages.

2. Fully Homomorphic Encryption (FHE): FHE supports arbitrary computations [130] on encrypted data, allowing any operation to be performed without decryption.

   • Example: Gentry’s FHE Scheme$$\begin{array}{c}\hfill Enc(f(m_1,m_2))=f(Enc(m_1),Enc(m_2))\end{array}$$ where f is any computable function, and $m_1$ and $m_2$ are messages.

Code-Based Cryptography

Code-based

Cryptography [131] relies on error-correcting codes for security, providing robust methods for securing communications and data storage.

• McEliece Cryptosystem:$$\begin{array}{c}\hfill c=mG+e\end{array}$$ where c is the ciphertext, m is the message, G is the generator matrix, and e is the error vector.

Lattice-based cryptography

Lattice-Based Cryptography [132] is based on the hardness of lattice problems, offering security against quantum attacks.

• Learning With Errors (LWE):$$\begin{array}{c}\hfill A·s+e=b\end{array}$$ where A is a random matrix, s is the secret vector, e is the error vector, and b is the resulting vector.

Multivariate Public Key Cryptography :

Multivariate Public Key Cryptography [133] (MPKC) uses multivariate polynomial equations as public keys, providing strong security and efficient computation.

• Multivariate Quadratic (MQ) Equations:$$\begin{array}{c}\hfill P(x)=x^{T}Qx+Lx+c\end{array}$$ where Q is a matrix, L is a vector, c is a constant, and x is the input vector.

Hash-Based Cryptography

Hash-Based Cryptography [134] leverages cryptographic hash functions for securing data and communications.

• Lamport Signatures:$$\begin{array}{c}\hfill Sig(m)=(h(s_1),h(s_2),...,h(s_n))\end{array}$$ where m is the message, $s_i$ are the secret keys, and h is the hash function.

Blockchain Applications in Edge Computing

Blockchain technology [135] has proven to be a useful tool for improving edge computing environments’ security. The decentralized nature of blockchain provides robust security features that are particularly beneficial in mitigating the vulnerabilities associated with edge devices [136].

Secure authentication

1. Immutable Records:  Blockchain ensures that all authentication records are immutable, meaning once data is recorded, it cannot be altered or deleted. This immutability prevents unauthorized modifications and provides a reliable audit trail.

2. Decentralized Control:  Unlike traditional centralized authentication systems, blockchain distributes control across multiple nodes. This decentralization [137] reduces the risk of a single point of failure and makes it more difficult for attackers to compromise the network.

3. Consensus Mechanisms:  Blockchain [138] uses consensus protocols such as Proof of Work (PoW) or Proof of Stake (PoS) to validate transactions and authentication attempts. These mechanisms ensure that only legitimate transactions are recorded, preventing fraudulent activities.

4. Cryptographic Security:  Blockchain relies on advanced cryptographic techniques [139] to secure authentication data. Public and private key pairs are used to authenticate users and devices, ensuring that only authorized entities can access the network.

5. Smart Contracts:  Self-executing contracts known as “smart contracts [140]” have their provisions encoded right into the code. They automate authentication processes, enforce security policies, and execute predefined actions when specific conditions are met, reducing the risk of human error.

Blockchain vulnerabilities

Although blockchain technology improves security, vulnerabilities still exist [141]. The following are some of the potential weaknesses in blockchain-based systems:

1. Smart Contract Exploits:   Vulnerabilities in smart contract code can be exploited by attackers to manipulate contract behavior [142]. Rigorous code auditing, formal verification, and adherence to best practices can mitigate smart contract vulnerabilities.

2. Consensus Protocol Attacks:  Consensus protocols [143] are critical to blockchain security, but they can be targeted by attacks such as 51 percent attacks, where an attacker gains control of the majority of the network’s computational power. Diversifying node ownership and using more secure consensus algorithms can reduce the risk of such attacks.

3. Sybil Attacks:  In a Sybil attack, an attacker creates multiple fake identities to gain disproportionate influence over the network [144]. Implementing identity verification mechanisms and limiting the influence of individual nodes can help prevent Sybil attacks [145].

4. Privacy Issues:  While blockchain provides transparency, it can also expose sensitive transaction data [146]. Techniques such as zero-knowledge proofs and confidential transactions can enhance privacy in blockchain networks.

Expanding the role of blockchain in securing edge computing

Blockchain technology not only guarantees security but has a much broader impact on the security and performance of all edge computing environments. Its decentralized, transparent, and tamper-resistant properties [147] provide a solid foundation to overcome some of the key challenges faced at edge computing in the process.

Data Integrity and Reliability: In edge computing, data is created and processed, often with no special capabilities [148]. Blockchain’s immutable ledger ensures that once data is recorded, it remains tamper-proof, authentic, and reliable in collecting data from edge nodes and this increasing accuracy is especially important in IoT ecosystems, where data from wearable devices are used to make important decisions. With a blockchain, any attempt to manipulate data is immediately reflected in the distributed network, creating trust in edge devices.

Decentralized information sharing: A key challenge in part computing is sharing statistics securely throughout allotted machines without counting on a critical server Blockchain gives decentralized records sharing, permitting aspect nodes to exchange secure records without failure now not even one [149]. This peer-to-peer sharing capability reduces latency and will increase community flexibility. Using blockchain, area computing systems can offer stable surroundings for depended-on users to trade touchy information together with clinical information or monetary transactions.

Access management and identity control: Blockchain has been broadly integrated into getting entry to manipulate structures in edge environments. It provides a method of managing identities and authorizations, and enables part devices to authenticate every other without a centralized authentication server [150]. This characteristic complements side network security, lowering the risks related to single points of failure around. Blockchain-based identification management can make certain that only authenticated and certified gadgets can get admission to the network, drastically decreasing the danger of unauthorized access.

IoT Device Security: Edge computing regularly is based on IoT gadgets, which can be regarded as having safety vulnerabilities. Blockchain strengthens the security of IoT gadgets by using presenting a secure framework for firmware updates, configuration control, and tool authentication [151]. Blockchain also can shop the virtual fingerprint of the tool’s firmware, permitting verification in opposition to a relied-on laser to ensure that the firmware has now not been tampered with this protection feature is important to ensuring IoT gadgets related to edge networks are not compromised.

IoT Security in Edge and Cloud Computing

The growth of the Internet of Things (IoT) [152] has delivered new protection-demanding situations in edge and cloud computing environments. The wider range of IoT devices in home, industrial, and important infrastructure settings makes them a top goal for cyberattacks. Their confined computational assets, regularly fragmented protection implementations, and steady connectivity further expose them to risks [153].

IoT security challenges in edge computing

Edge computing performs a vital role in processing statistics in the direction in which IoT gadgets perform, reducing latency and allowing faster decision-making [154]. However, the decentralization and distribution of part nodes create new protection dangers for the IoT structures that rely upon them. Unlike cloud computing, wherein sources and safety features may be centralized and controlled more effectively, side nodes and IoT gadgets frequently operate in less-managed environments, making them more prone to attacks [155]. Some of the important challenges in securing IoT devices at the brink consist of

• Limited Resources for Security [156]: IoT devices, through format, prioritize low electricity intake, small shape elements, and value performance over robust safety functions. Many IoT devices lack the crucial computational power to put into effect superior security measures which include actual-time encryption, multi-issue authentication [157], or modern-day anomaly detection algorithms. As a result, they’ll be susceptible to commonplace attacks consisting of device tampering, statistics spoofing, and guy-in-the-center attacks.

• Physical Vulnerabilities- IoT gadgets deployed in far [158]: flung or publicly available places may be bodily tampered with, most importantly due to unauthorized right of entry to or the injection of malicious software programs [159]. For example, in commercial IoT settings, such gadgets may manipulate crucial features like equipment or energy structures, and any breach ought to have significant outcomes.

• Inconsistent Firmware and Security Updates [160]: Many IoT gadgets are constructed with proprietary firmware, and updates to patch safety vulnerabilities are often not on time or not supplied at all. This creates a developing attack floor, in particular, while IoT gadgets are deployed in large numbers, as visible in clever towns or smart domestic environments [161]. Edge nodes processing statistics from those devices need to be ready with mechanisms to locate unpatched or compromised IoT gadgets and isolate them from the network to save you from similar harm.

• Data Integrity and Privacy Concerns [162]: IoT devices regularly acquire sensitive records, which include place records, fitness metrics, or economic transactions. When such data is processed at the brink, making sure its integrity and confidentiality is an assignment, in particular, while encryption requirements are not uniformly achieved throughout gadgets. Attackers have to intercept or manipulate facts at the threshold, essential to capability breaches [163].

IoT security in cloud computing

In a cloud computing environment, IoT safety takes on a distinctive dimension. While IoT devices generate massive amounts of facts at the brink, an awful lot of these statistics are ultimately transmitted to the cloud for further processing, storage, and analysis. Cloud computing gives extra centralized management over safety, but it also introduces new demanding situations for defensive IoT data and devices. Some of those demanding situations include the following.

• Centralized Data Storage Risks [164]: Cloud systems often preserve the records generated through IoT devices, and this creates a single factor of failure if no longer properly secured. A breach inside the cloud infrastructure ought to show information from heaps of IoT devices, leading to mass-scale statistics leaks [165]. Thus, securing records in transit (from part to cloud) and at rest (in the cloud garage) is critical. Encryption, facts overlaying, and getting the right of entry to manage mechanisms need to be applied across each part and cloud to guard sensitive records.

• Cloud resource sharing and multi-tenancy: Resources in cloud environments are often shared between multiple clients or applications. For IoT applications that rely on cloud resources, this can lead to capacity weaknesses if strict separation is not maintained between tenants [166]. Attackers may want to exploit vulnerabilities in shared objects or cloud hypervisors to gain access to IoT records from different residents Security mechanisms including sandboxing, virtualization protection, and micro-partitioning are needed to ensure IoT-is covered data so efficiently in multi-tenant cloud architectures

• IoT data life-cycle Management: As IoT devices provide a continuous flow of data, managing the life-cycle of data-i.e., [167] from data generation to storage and eventual deletion-drastically evolves the cloud, it forces reinforcing this complex garage, enforcing record-keeping rules, and deleting secure information once it is no longer wanted.

Insecurities It can allow attackers to gain access to or control sensitive ancient data.

Common threats to IoT security in edge and cloud environments

IoT gadgets, when integrated into both cloud and edge computing ecosystems, face quite a few threats which are compounded with the aid of their inherent barriers [168]. Some of the most prominent security threats consist of

• Botnet Attacks: IoT gadgets are the top goal for botnet attacks, in which attackers compromise big numbers of gadgets and use them to release dispensed denial-of-carrier (DDoS) attacks. The Mirai Botnet attack of 2016, as an example, exploited vulnerabilities in IoT gadgets, highlighting the vital need for more potent security controls in IoT ecosystems [169]. The distributed nature of side computing makes botnet attacks more difficult to detect and mitigate, as compromised IoT devices are spread across special places.

• Unauthorized Access: [170] Many IoT devices are deployed with default credentials or weak passwords, making them easy targets for unauthorized access. Attackers can exploit these weak access controls to take over devices, disrupt functionality, or use them as entry points to larger networks [171]. In both edge and cloud environments, stronger authentication mechanisms-such as public key infrastructure (PKI) and certificate-based authentication-are essential to prevent unauthorized access to IoT devices.

• Firmware Attacks: Since IoT devices often run on specialized firmware, attackers can cause vulnerabilities inside the firmware to gain manipulate over gadgets [172]. In aspect environments, compromised gadgets can infect special gadgets inside the network or control facts being processed via the threshold nodes. Regular firmware updates and monitoring for compromised gadgets are key to mitigating this risk.

• Data Exfiltration and Privacy Breaches: IoT devices accumulate massive volumes of touchy statistics, and attackers can make the maximum vulnerabilities in the communication channels amongst IoT gadgets, facet nodes, and the cloud to exfiltrate records [173]. Ensuring give up-to-surrender encryption and securing verbal exchange protocols (e.g., MQTT, CoAP) is vital to prevent information breaches in IoT systems.

Strengthening IoT security in edge and cloud computing

Given the scale and complexity of modern IoT systems, securing those gadgets across both cloud and edge environments requires a multifaceted approach.

• End-to-end Encryption: Data encryption should be carried out from the IoT tool to the cloud [174]. This ensures that although records are intercepted at any factor, they stay unreadable to attackers.

• Zero Trust Architecture [175]: Both edge and cloud environments can gain from a Zero Trust method, wherein no device, user, or service is inherently trusted, and entry is granted primarily based on continuous verification of identification and safety posture.

• Security Analytics and Anomaly Detection: Real-time monitoring and safety analytics can help find anomalous behavior in IoT gadgets [176]. Both edge and cloud systems need to lease devices getting to know algorithms to locate types of atypical activity, together with unauthorized access attempts, unusual records flow, or community intrusions.

• Regular Firmware and Security Patch Updates: Ensuring that IoT devices acquire everyday firmware updates and safety patches is critical to mitigating seemed vulnerabilities [177]. In area environments, automated mechanisms must be in the vicinity to push updates to faraway gadgets without user intervention.

• Dynamic Threat Modeling for Wireless Protocols : Unlike traditional cloud infrastructure, edge environments rely heavily on wireless communication protocols to transfer data between IoT devices and edge nodes [178]. Dynamic threat modeling can be used to continuously adapt security policies based on real-time traffic conditions and devices enable early detection of abnormal activity, such as unusual spikes in data flow or unauthorized access attempts. This model allows security systems to react faster to emerging threats before affecting application layers such as SQL databases, significantly reducing the window of opportunity for attackers.

• Edge device contextual knowledge : Edge device context awareness is an important aspect of security in IoT environments. Studies have shown that one of the major weaknesses in these areas is the lack of awareness of the device’s role in the network [179]. To address this, edge computing systems can benefit from implementing context-sensitive security measures. This policy will consider each device’s role in the network and its real-time status to inform the security policies applied to it. For example, if an edge device manages sensitive health information, its connections may be subject to stronger encryption and monitoring protocols than a device that monitors environmental conditions. This approach allows customized security measures, reduces unnecessary costs, and provides improved security where needed. Hacking research in IoT environments shows that one of the biggest vulnerabilities [170] lies in devices not knowing their contextual role in the network. Edge computing systems benefit from a context-sensitive security system [180], where each device’s activity and real-time status If equipped with a wearable device, its connections may be subject to strict encryption monitoring protocols if compared to a device that monitors environmental conditions. This differentiation, depending on the device context, will enable an appropriate approach to safety management, reducing unnecessary costs and increasing safety go-ahead where it matters most.

• Collaborative Edge-to-Cloud Security Models [181] : Current implementations on edge networks typically separate edge-to-cloud and device-to-edge communications using different security protocols [182]. A collaborative security model can be established where edge devices, edge nodes, and the cloud work together to monitor and react to threats at different network layers. A stated security approach together, for example, at the device layer (such as corrupted wireless data)., can detect anomalies [183], triggering local defensive actions at the stream and active threat mitigation at the cloud layer, ensuring complete system protection against wireless-based attacks [184].

• Active Firmware Integrity Check : A key vulnerability highlighted in IoT systems is regular maintenance and firmware updates. This challenge can be addressed with proactive integrity verification mechanisms that periodically check for firmware incompatibilities. This analysis can autonomously validate the integrity of edge device firmware in real-time using cryptographic hash functions [122], ensuring that devices running on old or corrupt firmware are flagged for immediate updates or quarantine This automated process on top of manual protection reduces dependencies, significantly reducing the risk of attacks due to unpatched vulnerabilities [185] on remote or hard to reach devices.

• Decentralization of security decisions [186]: Implementing security measures can be a significant breakthrough in IoT setups. Instead of all security measures being passed through a central authority, Edge devices can be powered for safety decisions based on pre-defined policies and real-time scenarios. Wireless communications, such as attempted man-in-the-middle attacks [187], are detected until the threat is resolved. This will enable faster response times to potential intrusions, especially on networks where latency or distance limits central control speed.

Dynamic Security Properties Monitoring Architecture for Cloud and Edge Computing

As the landscape of cloud and edge computing [188] continues to develop, the complexity of retaining security throughout both infrastructures turns into greater mentioned. Traditional security measures, at the same time as powerful in static environments, battle to hold tempo with the dynamic, on-demand nature of cloud and aspect deployments. The answer lies in Dynamic Security Properties Monitoring, a proactive method that constantly assesses security across those ecosystems.

Dynamic security in cloud computing

Cloud computing environments [189] are relatively elastic, permitting organizations to scale their assets up or down as needed. However, this flexibility also introduces new demanding situations for keeping security. As digital machines (VMs), containers, and services are created, moved, or decommissioned, their associated security houses-including person permissions, encryption protocols [190], and get right of entry to controls-should be continuously monitored and adjusted to save your vulnerabilities.

The Dynamic Security Properties Monitoring Architecture provides a framework for this. It operates by:

• Real-Time Threat Detection and Alerts [191]: Security breaches can happen at any moment, and traditional methods that rely on periodic checks or manual interventions are insufficient. Dynamic monitoring systems scan for abnormal behavior continuously, allowing for real-time alerts and faster threat detection.

• Automated Security Protocol Updates [192]: In cloud environments, wherein offerings and programs evolve hastily, manual safety updates can introduce delays or inconsistencies. Dynamic protection systems automate the manner of updating protection rules and measures based totally on the real-time country of the cloud infrastructure. For example, if a new service is provisioned, admission to controls, and firewall guidelines [193]are updated right away to mirror the state-of-the-art protection requirements.

• Resource Efficiency in Monitoring [194]: Given the size of cloud environments, efficient useful resource use is essential. Dynamic monitoring systems prioritize critical security occasions and focus sources on high-chance areas, making sure that the general system performance isn’t compromised using the security overhead.

Dynamic security in edge computing

While cloud environments process large amounts of data in centralized locations, edge computing processes [195] data outside the network close to the data source This decentralization provides lower latency but also poses additional security risks, because edge devices tend to operate in less controlled environments.

Dynamic security monitoring in edge computing enables organizations to manage these risks

• Localized threat response [196]: Since edge nodes handle critical, real-time data processing (e.g., in autonomous vehicles or smart grids), any delay in identifying or addressing security threats can result in it’s a terrible result Dynamic monitoring enables wearable devices to automatically detect and deactivating threats waiting for centralized cloud monitoring.

• Context-Aware Security Adaptation [197]: Different edge zones are often diverse, and different types of devices are used in different environments-from industrial to public spaces, security systems can tailor their care based on the unique circumstances of each defective device, recognizing that safety risks vary widely or

• Improved scalability and Efficiency: As the number of connected devices increases, security measurements across multiple edge nodes become more complex [198]. Dynamic security monitoring can scale up the network, ensuring that every new edge device automatically joins the security architecture without manual configuration This helps ensure consistent security coverage across the edge network.

  By leveraging dynamic security monitoring in both cloud and edge environments, organizations can ensure a holistic and flexible security posture that adapts to changing infrastructure needs and emerging threats.

Evolution monitoring of security properties for cloud and edge applications

In each cloud and edge environment, protection isn’t a one-time implementation-it evolves alongside the systems it protects. As packages and offerings undergo updates, migrations, or expansions, their protection requirements change [188]. Evolution Monitoring is a forward-looking method that anticipates those changes and ensures that security measures evolve in parallel with technological improvements and new dangerous landscapes.

• Evolution in Cloud Security: Cloud environments are characterized by their speedy tempo of exchange. Applications are frequently up to date, new services are delivered, and workloads are moved among unique areas or statistics facilities [199]. Each of those modifications introduces new security challenges that must be addressed.

The Evolution Oriented Monitoring framework ensures that cloud security remains agile and adaptive by:

• Continuous Assessment of Security Posture: progress monitoring is irregular; This requires regular assessment and development of security characteristics of cloud applications based on their operational flexibility [200]. For instance, whilst an application migrates from one cloud provider to another, associated safety features-along with information encryption standards or firewall settings-need to be reassessed and adjusted as a consequence

• Proactive threat modeling and risk mitigation: The main advantage of this approach is its ability to identify potential security risks before they become a real threat Analyzing historical data and current trends, evolutionary systems can identify areas where future security vulnerabilities arise and make adjustments ahead of time [201]. where security properties can be consistent across cloud environments.

• Security as a Continuous Process: By treating safety as a reactive process, where maintenance is implemented after a breach [202], progress monitoring establishes safety as an ongoing process when cloud applications are evolving with the security measures that protect them, ensuring that the system is always ready to deal with new threats.

Application to edge computing

In edge computing, the evolution of security properties is even more critical, as edge devices frequently operate in dynamic environments. With edge computing’s decentralized nature and heterogeneous hardware, security protocols must be flexible enough to evolve in real-time as edge devices are added, removed, or updated. Key advantages of evolution-oriented protection [203] tracking in edge computing include:

• Dynamic Device Management [204]: In part environments, gadgets may additionally frequently be part of or depart the network. Each new tool introduces ability protection risks, mainly in IoT deployments wherein part devices have restrained computational electricity [205] and may run outdated software programs. Evolution-oriented monitoring ensures that security properties-such as device authentication, data encryption, and communication protocols-are continuously updated to reflect the current state of the edge network.

• Synchronization with Cloud Security [206]: As edge devices often send data to centralized cloud services, it is essential that security measures at the edge evolve in tandem with those in the cloud. For instance, if a cloud service updates its statistics garage protocols [207] to include new encryption standards, the edge devices transmitting that information should additionally evolve to ensure compatibility and protection. Evolution-oriented monitoring presents the vital framework to synchronize safety guidelines among the cloud and the edge.

• Edge-Specific Threat Adaptation [208]: Edge environments are extraordinarily susceptible to localized threats, along with physical tampering or network spoofing, particularly in commercial or public settings. Evolution-orientated monitoring [209] allows side devices adapt their safety features primarily based on the present day chance panorama of their specific operational context. For instance, an facet device in a smart town would possibly need to regulate its protection residences in real-time primarily based on nearby cyber danger intelligence reports.

• Holistic Security Evolution [210] Across Cloud and Edge: As cloud and edge computing come to be more and more intertwined, keeping protection across those environments requires a unified, evolving technique. By integrating evolution-oriented monitoring [211], groups can ensure that protection protocols dynamically adapt across each the cloud and the edge, offering a comprehensive defense toward emerging threats.

In end, Evolution Oriented Monitoring guarantees that both cloud and edge safety continue to be fluid, responsive, and proactive, evolving to meet the security needs of the future.

We discuss various attacks suffered by the edge network in the following section(see sec. 3).

Attacks in Edge Computing

There is a significant growth in edge computing, which places computation and data storage closer to where it is required [212]. The advantages of this paradigm shift include reduced latency and improved bandwidth usage(see sec. 1). Nevertheless, it also poses new security challenges.

Various Attacks and Their Impacts on Edge Computing

We discuss the attacks and their impact on the edge computing framework in this section elaborately. We have mentioned how these attacks were performed and revealed the statistics on the frequency of these attacks.

Distributed denial of service (DDoS) attacks

In a DDoS attack [213], an aspect tool or community is flooded with internet visitors which will disrupt its ordinary functioning. These attacks can have a great impact on the availability of services leading to downtime hence denying legitimate users access to resources. Since they are distributed and often require little resource capacity, edge devices are prone to such kinds of attacks. Such an attack may occur at the network level on the edge computing architecture where devices connect to the internet and interconnect with other devices plus central servers [214].

Man-in-the-iddle (MitM) attacks

Man-in-the-middle attacks, also known as MitM attacks [215], happen when a third party listens in on two parties that are communicating and can change the content of the conversation. In aspect computing terms, this will mean that information is intercepted while moving among important servers and side gadgets resulting in information robbery or alteration. The incidents compromise statistics integrity in addition to confidentiality exposing full-size protection gaps. These attacks are possible within the conversation layer [216] where statistics are dispatched among area devices, gateways, and significant servers.

Data breaches

Data leakage can happen if anyone gets unauthorized access to personal information handled or stored on the edges of networks [217]. Often, these devices handle sensitive details such as weak encryption, vulnerabilities in device security or poor access controls that might lead to a breach. The financial costs for an organization which has been affected by a cyber attack can be enormous [218]. These types of attacks can occur in the data storage and processing layer through which sensitive information is kept on edge devices before being processed.

Device Hijacking

This involves gaining control of and using a device to conduct evil activities such as launching further [219]. Device hijacking is where a compromised device is used as part of a botnet for DDoS attacks or other malicious behavior. Mostly they take advantage of weak points in the security of devices often through obsolete firmware or not secured configurations. At the device layer, which encompasses individual edge devices, it can be done [220].

Physical attacks

Physical tampering with edge devices due to their deployment in less secure, often remote locations can lead to physical attacks [221]. These types of attack involve direct access to hardware and thereby enables attackers modify or destroy the device. Physical security measures should be put in place to mitigate these kinds of attacks that could cause data loss, device malfunctioning among others leading to unauthorized data access. In this case, it occurs at the bodily stage wherein side gadgets are deployed physically.

Firmware and software exploits

Exploiting software weaknesses or security vulnerabilities in the firmware or software that operate on the edge gadgets can deliver unauthorized people get right of entry to, enable them to execute arbitrary codes, or strengthen privileges [222]. The chance of such exploits may be mitigated by using ordinary updates and patch control. Any software and firmware that underlies tool operations make it possible for this sort of assault to manifest.

Eavesdropping

Eavesdropping attacks arise when information is intercepted unlawfully as it’s miles being transmitted between side devices and other network additives [223]. Such attacks are capable of capturing sensitive information including personal data, login credentials, and intellectual properties (IPs). Preventing eavesdropping from happening involves encrypting transmitted data. Such a form of attack is doable within the communication layer where data is sent across networks.

Data tampering

Tampering with records processed or stored on area devices can result in inaccurate or harmful effects [224]. Altering information can compromise the integrity of touchy facts, resulting in wrong picks or systematic errors. Such assaults can occur on the data garage and processing stage, in which facts integrity is vital.

Side-channel attacks

The use of bodily traits along with cutting-edge consumption or power to access sensitive person records is referred to as aspect-channel attacks [225]. These assaults can bypass traditional safety systems around through reading the bodily properties of a device at some stage in operation. Defending against side path attacks requires specialized countermeasures and secure hardware configuration. Such attacks can occur at the physical level, where attackers can access devices directly.

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Fig. 7

Distribution of various cyber attacks over the year 2023

Fig. 7 [226] depicts the distribution of numerous assaults that targeted edge networks in 2023, supplied as percentages of the overall number of cyber-attacks recorded for the duration of the 12 months. The most typical attack type changed into Man-in-the-Middle (MitM) [227], constituting 18.1% of the overall assaults, observed with the aid of Eavesdropping at 14.2%. Data Breaches accounted for 12.2%, and Data Tampering made up 11.4%. Device Hijacking represented 10.2%, even as Firmware Exploits were 8.1% of the whole. Side-channel attacks had been 6.1%, Physical Attacks were 4.9%, and Distributed Denial of Service (DDoS) attacks have been the least not unusual at 3.1%. This distribution underscores the various and multifaceted nature of cybersecurity threats encountered in 2023.

[See PDF for image]

Fig. 8

Sector Wise Attacks in the year 2023

Fig. 8 illustrates how data breaches were distributed across different sectors in 2023. It highlights that the Healthcare sector was the most affected, with 35% of breaches occurring in this field. The Finance sector experienced 25% of breaches, while the Education sector saw 20%. Both the Retail and Technology sectors were equally impacted each accounting for 10% of the breaches. This distribution underscores the varying levels of vulnerability across different sectors to data breaches during the year 2023.

In the next subsection, we will review several significant attacks that have occurred since 2010, with a particular focus on the Mirai Botnet attack, which is noted as one of the most severe cyber attacks of the past decade.

Notable Real-Time Attacks on Edge Network

Edge computing has seen many notable attacks over the years. We discuss these real-time, noteworthy attacks in this section.

Mirai Botnet Attack (2016)

The Mirai botnet assault [228] leveraged thousands of compromised IoT gadgets, many of which acted as edge gadgets, to launch a massive Distributed Denial of Service (DDoS) assault [229]. This attack disrupted main websites and services, including Dyn, a DNS service issuer [230]. The assault brought on good-sized net outages, affecting users across the globe and exposing vital vulnerabilities in IoT and edge gadgets. It highlighted the convenience with which susceptible or default passwords will be exploited. The sheer scale of the assault, regarding as much as 600,000 gadgets, showcased the monstrous power of coordinated botnets [231] and the vulnerability of unprotected IoT ecosystems.

VPNFilter Malware (2018)

VPNFilter [232] malware infected more than 500,000 routers and network devices internationally. The malware changed into being able to intercept and exfiltrate facts, injecting malicious code, and rendering devices inoperable. The attack affected a huge variety of gadgets from exceptional manufacturers, causing a giant disruption to internet offerings and statistics integrity. VPNFilter’s [233] multi-stage attack approach, which included continual and non-chronic modules, made it notably hard to eliminate and highlighted massive security gaps in a router and network tool firmware.

Stuxnet Worm (2010)

While Stuxnet [234] did not concern edge computing, what is interesting to note is that other worms such as this one breached the air gap and compromised industrial control systems before zero-day vulnerabilities of hosts on the very edges of corporate networks came under attack. The cyber worm ’stuxnet’ tangibly damaged the Iranian nuclear program, changing the velocity of centrifuges and showing that hardware-specific attacks could be used to cause physical damage. This has illustrated not just the significance of securing industrial edge devices, but also how widespread cyber warfare can be. The sophistication of Stuxnet-using zero-day exploits and tightly directed to single targets like the centrifuges in Natanz [235], Iran’s uranium-enrichment facility (with code earmarks pointing back to Israel)-raised questions about whether nation-states were using cyber-war as an extension tool of state policy or whether vulnerabilities critical infrastructure had just started being exposed.

Triton/Trisis Malware (2017)

The Triton/Trisis [236] malware was identified, which hit an oil refinery in Saudi Arabia back in December 2017. The malware was designed to tamper with Triconex Safety Instrumented System (SIS) [237] controllers made by Schneider Electric [238]. Using the SIS controllers, they modified code in an attempt to disrupt safety processes and cause a physical incident. Fortunately, the possible harm was stopped by a system shutdown before any physical damage could be sown but still at an estimated $50 million cost for material response to mitigation. The factory was the subject of many production halts, including a full shutdown for weeks on end to investigate and fix the breach. This attack raised serious concerns about the security of industrial control systems, affecting the reputation of Schneider Electric and highlighting vulnerabilities in critical infrastructure.

NotPetya Attack (2017)

The NotPetya ransomware assault [239] came about in June 2017, broadly speaking affecting agencies in Ukraine but fast-spreading globally. Initially delivered through a malicious replacement to a popular Ukrainian accounting software program, NotPetya [240] exploited vulnerabilities in Windows systems to propagate unexpectedly throughout networks. The ransomware encrypted files and demanded a ransom fee, however, the primary motive was regarded to be disruption in preference to monetary advantage. Estimated worldwide monetary losses handed $10 billion, with most vital multinational agencies like Maersk [241], FedEx, and Merck reporting massive effects. The assault triggered considerable operational disruptions, major to business corporation continuity demanding situations and loss of productiveness. Maersk, for instance, had to reinstall 4,000 servers and 45,000 PCs, and FedEx counseled a $400 million impact on its operations.

Colonial Pipeline Ransomware Attack (2021)

The Colonial Pipeline ransomware assault [242] befell in May 2021, focused on the most important fuel pipeline device in the United States. The assault was done by using the DarkSide ransomware organization [243], which received entry to Colonial Pipeline’s community through a compromised VPN account. The attackers encrypted crucial data and demanded a ransom for its release. Colonial Pipeline paid a ransom of $4 million in cryptocurrency to the attackers, and the entire cost, consisting of operational losses and mitigation efforts, is anticipated to exceed $100 million. The pipeline closed down for several days, fundamental to gas shortages, and the rate will boom at some stage in the Eastern United States. The disruption affected diverse industries dependent on gas factors. The incident highlighted vulnerabilities in vital infrastructure and caused advanced scrutiny of cybersecurity practices [244] inside the power region. Colonial Pipeline confronted reputational damage and crook repercussions, prompting investments in stepped-forward cybersecurity measures and reaction capabilities.

Impact of the Attacks on Edge Network

The damages due to diverse cyber assaults on aspect networks are massive and multifaceted [245]. This section presents a detailed analysis of the economic losses, operational disruptions, reputational harm, and bodily damage attributable to those assaults.

Financial Losses

The Mirai botnet attack in 2016 [246] added high-quality economic losses, predicted at around $110 million. This occurred because of considerable carrier disruptions that affected essential online systems and offerings, leading to operational downtime and a lack of revenue. The assault’s impact prolonged businesses and groups reliant on net infrastructure, highlighting the giant financial expenses associated with huge-scale DDoS assaults [247]. Companies such as Dyn, a chief DNS provider, have been in particular hard-hit, resulting in cascading effects across several websites and offerings. The financial repercussions extended past instantaneous losses, encompassing charges for mitigation, healing, and implementation of improved safety features to prevent future incidents. The Triton/Trisis malware [248] assault’s capability damage became mitigated via a tool shutdown earlier than any physical harm may also need to arise, but the expected financial cost for mitigation and reaction end up round $50 million. The NotPetya attack [249] caused worldwide financial losses exceeding $10 billion, with most important multinational groups like Maersk, FedEx, and Merck reporting tremendous effects. Maersk, as an example, had to reinstall 4,000 servers and forty 5,000 PCs, and FedEx pronounced a $400 million effect on its operations. The Colonial Pipeline ransomware attack led to economic losses exceeding $100 million, which includes the ransom payment of $4.4 million in cryptocurrency, operational losses, and mitigation efforts.

Operational Disruption

In 2018, the VPNFilter [250] malware attack caused significant business disruption for businesses and people relying on switched-on devices. More than 500,000 routers and network gadgets were destroyed, causing severe damage to net offerings and data integrity. The malware’s ability to freeze and delete records, inject malicious code, and disable devices caused severe performance problems. Critical projects faced downtime, and teams had to invest in large assets to repair the affected buildings. The performance disruption highlighted vulnerabilities in party and local devices and underscored the importance of strong security practices in the form of simple firmware updates and comprehensive network management

The Triton/Trisis [251] malware attack brought on sizable operational disruptions, which included an entire shutdown of the petrochemical plant for an extended length to research and remediate the breach. The NotPetya assault [252] precipitated significant operational disruptions, leading to commercial enterprise continuity challenges and a lack of productivity. The Colonial Pipeline ransomware [253] assault brought about the shutdown of the pipeline for several days, causing gas shortages and rate increases across the Eastern United States.

Reputational Damage

Companies affected by data breaches and phishing incidents often suffer significant reputational damage, affecting customer trust and business relationships. For example, the Mirai botnet attack [254] led to a loss of trust in IoT device manufacturers due to weak passwords or defaults used on these devices and customers began to question the security of their devices, causing sales and their reputation to decrease. Similarly, the VPNFilter attack [255] tarnished the reputation of router and network equipment manufacturers, as they exploited vulnerabilities in their products Reputation damage extends beyond immediate financial loss, affects long-term customer loyalty and market positioning, and requires substantial PR efforts to restore trust. The Triton/Trisis malware attack [256] raised serious concerns about the security of industrial operating systems, affecting Schneider Electric’s reputation and exposing significant industry vulnerabilities. Companies affected by the NotPetya attack suffered reputation damage due to their inability to contain the attack and recover quickly. The Colonial Pipeline ransomware attack [257] has increased scrutiny of cybersecurity practices in the energy sector, resulting in reputational damage and legal repercussions for Colonial Pipeline.

Physical damages

The impact of the Stuxnet worm on physical infrastructure suggests that cyberattacks can wreak havoc on the real world, extending beyond the digital domain. It was discovered that in 2010, Stuxnet targeted the Industrial Control System (ICS) [258]. so especially. The destructive potential of the processors has been demonstrated, and the weaknesses of remote industrial devices have been exposed. The physical damage caused by Stuxnet is estimated to be worth more than $1 billion and highlights the need for increased security measures in technology this case highlighted the need to create ICS and SCADA (Supervisory Control and Data Acquisition) [259]. Emphasize the protection of the system against similar threats in the future.

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Fig. 9

Comparison of Damages from Major Cyber Assaults

We describe a comparative study on the impacts of the real-time attacks on edge computing in Fig. 9; Mirai Botnet (2016) [260], VPN Filter Malware (2018), Stuxnet Worm (2010), Triton/Trisis Malware (2017), NotPetya Attack (2017), and Colonial Pipeline Ransomware (2021). The Mirai Botnet [261] assault led to monetary losses expected at $110 million, caused essential websites and offerings to head down, and caused a loss of agreement with IoT device manufacturers. The VPN Filter [262] Malware attack brought on financial losses of $one hundred million, disrupted net offerings, impacted router, and network tool vendors, and took approximately potential records robbery and device incapacitation. The Stuxnet [263] Worm assault brought about over $1 billion in monetary losses, disrupted business operations, affected country-wide safety, and caused physical harm to nuclear centrifuges. The Triton/Trisis Malware attack resulted in financial losses estimated at $50 million, significant operational disruptions, and reputational damage to Schneider Electric. The NotPetya attack caused global financial losses exceeding $10 billion, widespread operational disruptions, and reputational damage to affected companies. The Colonial Pipeline ransomware attack led to financial losses exceeding $100 million, significant operational disruptions due to fuel shortages, and reputational damage to the energy sector.

Once we explore the scopes of various attacks on man edge computing networks, it is high time, we focus on the detection and prevention of these attacks on an edge network. We describe various measures to detect an attack on an edge network. We also provide several solutions to combat the attacks on an edge network.

Detection of Edge Computing-Based Attacks

Detecting whether an edge network is attacked by a third party is crucial to combat the immediate damages caused by the particular attack. In this section, we initially discuss how to detect if an attack has taken place in the edge network(see sec. ??). In sec. 5 we depict how we combat against these attacks and sustain the safety and reliability of the edge network. We have also done some experimentation by ourselves to project how this detection of attack is done in the edge network. These simulation results are making our research novel and cutting-edge in terms of technicality.

As edge computing continues to gain traction in various industries, from healthcare to smart cities, the need for robust and effective attack detection mechanisms has become paramount. This section delves deeper into the challenges of detecting attacks on edge networks, explores advanced detection techniques with real-time examples, and provides comparisons between these techniques using recent data.

Exploring Various Attack Detection on an Edge Network

The detection of attacks in edge networks requires a combination of techniques, each tailored to address specific challenges [264]. This section explores these techniques in detail, providing real-world examples and comparing their effectiveness.

Anomaly detection

Anomaly detection is a method of identifying deviations from the Hook sub-pattern of routine behavior in a community. This method is particularly useful for unknown attacks or any date entry that does not match any known signature.

1. Machine Learning (ML): ML algorithms are broadly used for anomaly detection in aspect networks. For instance, deep knowledge of models can be educated to understand everyday site visitor styles and perceive anomalies in real time [265]. A case study in smart grid systems tested the effectiveness of ML in detecting cyber-physical assaults by way of studying actual-time facts from sensors. However, the fulfillment of ML-primarily based detection depends on the availability of categorized facts and the ability to evolve to changing network conditions [266]. Recent studies indicate that ML fashions can reap an accuracy of over 95% in detecting anomalies, however, additionally, they require substantial computational sources, which can be challenging for aid-constrained side gadgets.

2. Statistical Methods: These techniques use audit techniques to create outsiders in network traffic [267]. For example, in an intelligent enterprise system, Statistical System Manipulate (SPC) can be used to confirm the performance of edge devices and identify obstacles that may indicate an attack. While much less computationally in-depth than ML, statistical techniques won’t be as powerful in detecting state-of-the-art, multi-level attacks [268]. A contrast between statistical methods and ML methods is that whilst statistical methods are quicker and require fewer sources, they have a better fake-fine fee, especially in complicated environments.

Table 2. Comparison of Anomaly Detection Techniques

Technique	Accuracy	Resource Requirements	False Positive Rate	Example Use Case
Machine Learning	High (95%+)	High	Low	Smart grids, autonomous vehicles
Statistical Methods	Moderate	Low	Moderate to High	Smart factories, basic IoT deployments

Table. 2 provides a side-by-side comparison of numerous anomaly detection techniques used in edge computing. It evaluates the strategies based on accuracy, resource requirements, false positive Rate, and example use cases [269]. Machine studying strategies show off high accuracy however call for enormous computational assets, making them best for complicated environments like smart grids. In contrast, statistical techniques, although much less useful and resource-intensive, may additionally have higher false positive quotes and are ideal for less complicated IoT deployments.

Signature-based detection

Signature-primarily based detection entails figuring out recognized assault styles by comparing incoming visitors in opposition to a database of attack signatures [270]. This method is effective toward recognized threats but may additionally struggle to stumble on 0-day assaults or novel variations.

1. Intrusion Detection System (IDS): IDSs are commonly used on facet networks to monitor visitors and sense intercepted attack signatures [271]. For example, in a retail base network, an IDS may encounter calculable malware signatures between data transmission point-of-sale (POS) systems and critical servers. The mission with IDSs lies in maintaining an up-to-date signature database, mainly in swiftly evolving risk landscapes [272]. Recent improvements in IDS generation have stepped forward their detection charges, however, they still depend heavily on signature databases, making them much less powerful in opposition to new or unknown threats.

2. Rule-based detection: This approach uses custom rules to stumble upon attacks. For example, any chat code from a particular IP address change that is found to be associated with a malicious interest could be flagged [273]. Although primary rule detection is simple and easy to implement, it can produce false positives, especially in dynamic areas such as lateral interfaces Fully rule-based distinctions are signatures that depend primarily on detection in the healthcare IoT network And have a higher false-positive cost compared to -based-totally-identity which is more accurate.

Table 3. Comparison of Signature-based Detection Techniques

Technique	Detection Rate	Maintenance Effort	False Positive Rate	Example Use Case
Intrusion Detection	High	High	Low	Retail networks, enterprise networks
Rule-based Detection	Moderate	Low	Moderate to High	Healthcare IoT, smart homes

Table. 3 outlines the key challenges faced in detecting attacks on edge networks, such as limited processing power, decentralized data, and the heterogeneity of devices [274]. Each challenge is rated by severity and impact on security. The table highlights the significant constraints imposed by the edge environment, making traditional detection methods less effective.

Behavioral analysis

Behavioral evaluation involves tracking the behavior of customers, devices, and applications within the network to become aware of capacity threats [275]. This method is mainly effective in detecting insider threats and superior continual threats (APTs).

1. User and Entity Behavior Analytics (UEBA): UEBA solutions examine the conduct of users and devices to locate anomalies that may imply an assault [276]. For example, in an economic services facet network, UEBA should detect unusual rights of entry to styles that advocate a compromised account. A case look at a large financial institution showed that UEBA should hit upon over ninety% of insider threats, making it a treasured tool in securing facet networks. However, UEBA requires continuous tracking and the established order of conduct baselines, which can be aid-intensive.

2. Endpoint Detection and Response (EDR): EDR responds to screening of endpoint play and allows unique detection in endpoint conduct. In industrial IoT surroundings, EDR prefers to create suspicious activities on peripheral devices, including unauthorized firmware adjustments [277]. EDR has tested effective in detecting complex assaults, but it requires high-priced energy and might yield large amounts of records, making it difficult to use in area gadgets with restricted resources even though it is.

Table 4. Comparison of Behavioral Analysis Techniques

Technique	Effectiveness	Resource Requirements	Complexity	Example Use Case
UEBA	High	High	High	Financial services, critical infrastructure
EDR	Moderate to High	Moderate to High	Moderate	Industrial IoT, smart manufacturing

Table. 4 summarizes six notable real-time attacks on edge computing from 2010 to 2023, detailing the attack method, affected systems, impact, and financial losses [278]. It showcases the increasing sophistication and impact of cyberattacks over time, emphasizing the need for advanced security measures in edge networks.

Collaborative detection

Due to the distribution of the feature networks, collective detection methods have gained popularity [279]. These approaches leverage the capabilities of synergies and data on the network, two devices that work together to detect and respond to threats

1. Federated Learning: Federated learning enables a couple of facet gadgets to collaboratively teach a global machine mastering version without sharing their records. For instance, in a clever city environment, federated learning may be used to come across anomalies in traffic patterns across extraordinary areas of the city [280]. This approach enhances privacy and reduces the danger of statistics breaches. Recent research has proven that federated mastering can reap comparable accuracy to centralized fashions whilst significantly reducing the communication overhead between gadgets.

2. Distributed Intrusion Detection System(DIDS): DIDS works on a couple of side devices, stocks data, and coordinates responses to detected threats [281]. In an allotted electricity network, DIDS can become aware of site visitors throughout a couple of side nodes, and hit upon coordinated attacks which include dispensed provider denial (DDoS) attacks DIDS has unique talents in big, distributed environments with centralized detection that do not paint. However, state-of-the-art synchronization techniques and strong mechanisms for exchanging statistics between devices are required.

Table 5. Comparison of Collaborative Detection Techniques

Technique	Scalability	Privacy Preservation	Detection Acuracy	Example Use Case
Federated Learning	High	High	High	Smart cities, connected vehicles
Distributed IDS	Moderate	Moderate	Moderate to High	Distributed energy grids, smart factories

Table. 5 presents an assessment of the financial, operational, reputational, and physical damages caused by key attacks on edge computing networks. It quantifies the damages in monetary terms and operational downtime, offering a clear picture of the consequences of these attacks [282]. The table underscores the substantial risks posed by cyber threats to both digital and physical infrastructures. Next, we discuss about the challenges faced while detecting an attack on the edge network.

Challenges in Detecting Attacks on Edge Networks

Edge computing networks, by using their very design, pose unique challenges for attack detection [283]. These demanding situations stem from the character of edge devices, the disbursed architecture of the networks, and the varying tiers of safety talents across gadgets. We discuss all these constraints and challenges below.

1. Resource Constraints:   Edge gadgets frequently have confined processing power, reminiscence, and battery existence, which constrains the implementation of comprehensive security measures [284]. For instance, a small sensor node in a business IoT network may lack the computational potential to run complex anomaly detection algorithms in actual time. This dilemma is clear in instances like the Mirai Botnet attack (2016)(see sec. 3.2.1), where compromised IoT gadgets, lots of which were aid-confined, were used to release a huge DDoS attack [285]. The inability of those gadgets to enforce superior safety protocols made them easy goals.

2. Data classification and quantification:  The decentralized nature of the facet computing approach is that information is processed at more than one location, normally close to the information source [286]. This decentralization complicates the venture of tracking and analyzing traffic in the community. A real-global example of this mission may be visible in a clever town [287], wherein statistics from heaps of sensors spread throughout the city have to be ruled out as a capability danger.

3. Inconsistency of equipment:  Edge networks include a wide range of devices, from simple sensors to complex industrial systems. This diversity creates a complex security environment where traditional, one-size-fits-all security solutions are not enough [288]. For example, VPNFilter Malware (2018)(see sec. 3.2.2) targeted a wide range of devices, including routers from different manufacturers, which demonstrated how difficult it is to build a universal identification mechanism that can protect all devices on an edge network in.

4. Latency Sensitivity  One of the number one advantages of edge computing is its capacity to lessen latency with the aid of processing records in the direction of the supply. However, this equal characteristic also can lessen the time to be had for detecting and responding to attacks [289]. In time-touchy applications, including self-sufficient cars or remote healthcare tracking, even a slight delay in assault detection ought to have severe effects. The Stuxnet Worm (2010)(see sec. 3.2.3), whilst now not a part-unique attack, highlighted the ability risks associated with behind-schedule detection, because the bug operated undetected for an extended period, causing sizeable bodily harm.

Prevention of Attacks on an Edge Computing Network

As edge computing gets traction, its decentralized architecture raises serious security concerns. The expansion of connected devices at the network’s edge increases vulnerability to cyberattacks, posing a threat to sensitive data and key services. To avoid these risks, it is critical to establish effective prevention techniques that are specific to edge situations. This section describes critical techniques for enhancing security and protecting against potential threats.

Exploring Various Attack Prevention Procedures on an Edge Computing Network

In this section, we discuss various procedures that has to be followed to prevent the attacks on edge computing.

Secure authentication and access control

Multi-Factor Authentication [290] (MFA): By requiring multiple forms of verification, MFA increases the security around accessing network resources. This combines something they know (password), something they have (a mobile device) and possibly even a third factor like biometrics.

For example, edge devices in a smart city infrastructure can use MFA to make sure that only authorized persons could access sensitive data or control systems attitude.

Role-Based Access Control (RBAC): [291] RBAC determines what resources are available to employees based on their jobs within the organization. Sensitive data [292] is only exposed to legitimate parties

Use case: In a healthcare IoT scenario, doctors can get the patient records whereas maintenance staff just need access to network settings.

Secure software updates

Over-the-Air (OTA) Updates: [293] Most edge devices need to be patched quite regularly, in order to fix security vulnerabilities and/or improve functionality. The OTC update system makes sure that the only updates which are to be installed in your device is the one being authorized, therefore it will be difficult for a malicious actor to introduce its own firmware into an already secure OTA [294] image.

Verification of Consistency: Security Guidelines prescribe that any updates are validated by the device as consistent update package which can be achieved using methods like cryptographic hashing [295] before applying to rest parts. This provides some degree of integrity and ensures that the update has not been tampered with in transit.

Rollback Capabilities: A secure rollback capability allows a device to revert to the last known good or stable version in case of any issues with an update - where as if for example, vulnerabilities are discovered after the recent upgrade.

Intrusion detection and prevention systems (IDPS)

[296] Real-Time Monitoring: IDPS answers constantly screen community traffic and machine sports for signs of malicious behavior, including uncommon site visitors styles, unauthorized get entry to tries, or recognised assault signatures. By figuring out threats in real-time, businesses can respond swiftly to mitigate dangers.

Behavioral Analysis: Advanced IDPS use behavioral evaluation to hit upon anomalies based on ancient facts. This enables in identifying new, formerly unknown threats that might not be detected via conventional signature-based totally strategies.

Automated Response: Some IDPS [297] solutions are equipped with computerized reaction mechanisms that can isolate compromised gadgets, block malicious visitors, or trigger signals to security teams for similarly investigation.

Regular Security Audits and Penetration Testing:

[298] Vulnerability Assessments: Regular safety audits assist become aware of vulnerabilities within the side computing surroundings. These tests involve reviewing security configurations, software program versions, and get right of entry to controls to make sure that they meet present day protection requirements.

Penetration Testing: Simulated attacks, referred to as penetration assessments, help perceive weaknesses inside the device through mimicking the strategies used by actual attackers. This proactive technique lets in groups to cope with vulnerabilities before they can be exploited inside the wild.

Compliance Audits: For industries with unique regulatory requirements, everyday compliance audits make sure that the edge computing infrastructure adheres to standards along with GDPR, HIPAA, or PCI-DSS [299], lowering prison and monetary risks.

Anomaly Detection Using AI/ML

Pattern Recognition: AI and machine learning algorithms [300] can examine vast amounts of data to stumble on styles that can imply safety threats. These systems can pick out subtle changes in conduct that would cross not noted by conventional safety features.

Adaptive Security: AI/ML [301]-pushed safety structures can adapt to new threats by using getting to know from beyond incidents. This non-stop knowledge of manner improves the accuracy and effectiveness of hazard detection over the years.

Physical Security

Securing Edge Devices: Edge devices are regularly deployed in environments wherein they may be more liable to bodily attacks. Implementing physical security features along with tamper-glaring seals, secure enclosures, and surveillance can deter unauthorized get admission to and tampering.

Environmental Controls: In addition to protecting against tampering, ensuring that edge gadgets are in environments with controlled temperature, humidity, and energy deliver is crucial to hold their operational integrity and save you failures that would lead to protection vulnerabilities.

Access Control to Physical Locations: Limiting entry to the physical places [302] wherein gadgets are deployed via bio-metric authentication, security employees, and get entry to logs facilitates in ensuring that handiest authorized people can engage with the hardware.

Network Segmentation

Micro segmentation: Network segmentation can be applied at a granular stage via micro segmentation [303], wherein person workloads or applications are divided from each other. This minimizes the danger of lateral movement through attackers inside the community, as every segment is independently secured.

Virtual LANs (VLANs) and Firewalls: By the use of VLANs and firewalls [304], one-of-a-kind components of the community may be divided based on function, sensitivity, or threat level. Firewalls can put in force strict get entry to manage rules between segments, in addition reducing the risk of assaults spreading.

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Fig. 10

Effectiveness of Security Measures in Edge Computing

Fig. 10 shows the comparative efficiency of different security measures used in edge computing environments for preventing attacks. Each bar represents a different level of security, with the height of the bar corresponding to its effectiveness at exposing potential security threats, and expressed as a percentage.For example, Multi-Factor Authentication (MFA) [305] shows an efficiency of 99.9%, which means it can prevent unauthorized access. Secure software updates follow closely behind with 98% effectiveness, emphasizing their role in preventing firmware changes. In contrast, Role-Based Access Control (RBAC) has a capacity drop of 40%, indicating a typical use case for reducing unauthorized access based on the roles used.This Figure also highlights other important measures such as Intrusion Detection and Prevention System (IDPS) 95% , anomaly detection using AI/ML [306] is 90%, routine security auditing and penetration testing is 80%, and network segmentation is 75%, showing their contribution to advanced security planning at the edge in computing.

In the following sections(refer to sec. 6, we discuss the technique that simulates a secure alternative to an insecure login system that is susceptible to SQL injection attacks, thereby illuminating the security problems associated with edge computing. The main distinctions between safe and insecure login implementations in an edge computing setting are outlined.

Challenges in Preventing Attacks on An Edge Network

Preventing attacks on edge networks has unique challenges due to the decentralized structure, diverse devices, and limited security resources for the edge networks. Inconsistent update mechanisms, lack of standardized protocols, and energy constraints further complicate protection. The following are the Key challenges in securing edge environments.

1. Incompatible software update mechanism: [307] Many edge devices do not allow automated updates or patches, making it impossible to distribute essential security changes reliably across the network. If older devices are not patched, they become vulnerable.

2. Lack of formalized security policies: While centralized cloud computing has some well-established security standards, edge computing does not have a widely accepted policy [308], leading to inconsistencies in how security is implemented.

3. Local Processing: Processing data at the edges raises data privacy concerns, especially if devices process sensitive information locally. Ensuring data privacy without compromising security is difficult, especially with privacy laws that vary from region to region.

4. Challenges to incident response and forensic investigation: When a security breach occurs, it can be extremely difficult to pinpoint the source due to the distributed nature [309] of the edge network. These challenges for incident response and forensic investigation are slowed, increasing recovery time.

5. Energy constraints in security applications: Some edge devices, especially IoT sensors, operate with strict energy constraints [310], limiting their ability to perform energy-intensive security measures like continuous encryption or complex authentication.

6. Data integrity across multiple edge layers: Ensuring data integrity as it travels across different edge layers (e.g., from device to local edge node to central cloud [311]) is challenging due to potential data changes or loss in transit. Therefore, more sophisticated data management and validation techniques are required.

7. Insider Threats at the Edge: With edge devices close to users or operators, insider threats [312] pose a significant challenge. Insiders with physical access or operational control over equipment can bypass security measures.

Simulating an attack on Edge Network

Once we understand the basics of edge computing, and the threats and prevention in an edge computing network, we try to study it in a real-time scenario. Therefore, we simulate an attack on the edge computing network and then depict how this attack is detected and prevented thoroughly. Our entire simulation of SQL injection attack is provided step by step as follows.

• Initially, we design a web application to provide an SQL injection attack on an edge network.

• Next, we set up a database that contains the user ID and passwords of all the trusted users who frequently access the system.

• Now when a user tries to log in, he or she uses their credentials to log in. Once they provide their credentials for logging in to the application, their credentials are matched with the back-end database.

• The target of the attacker is to tamper with this database so that the attacker can access the application easily.

• We have used the input sanitation method to prevent this attack on an edge network and bolster the security of the edge network.

[See PDF for image]

Fig. 11

Flow Chart for how application works

We have provided a detailed insight of each of these steps of Fig. 11 in the following subsections.

Experimental Setup

The test environment was designed to accurately simulate a real-world scenario where SQL injection attacks could be launched against web applications. We used Python’s lightweight web development tool called Flask [313] to develop a web application for simulating SQL injection attacks on an edge network. Several input fields in the program allow users to query data stored in the backend database. The database was created using a dictionary-based data structure [314] in Python, which stored sensitive data such as users and passwords, which made it easier to use and control test scenarios, we can implement safely to ensure that the application relies on SQL injection attacks and insecure versions of this simulated environment [315]. The test bed [316] was installed on a Linux-based system [317] in order to take advantage of the availability of open-source security tools [318]. To simulate a more realistic database environment, we ran a MySQL database [319] on a separate server in the second part of the experiment. The web application was accessed through web browsers (Chrome, Firefox) to ensure that specific browser actions would not interfere with the test results 2. Controlled Variables Controlling the following variables was key to testing the vulnerability [320] of the application to SQL injection attacks and measuring the success of the various security implementations:

SQL Injection Techniques

1. Error-based SQL injection: This involved executing queries that deliberately generate database errors [321], and expose configuration issues.

2. Association-based SQL injection [322]: Combining data from different SELECT queries to obtain unauthorized information. Boolean-based (blind) SQL injection: Logical states are injected to modify the flow of data without generating visual errors, this technique is often used when preventing error reporting.

3. Input Field Configuration:

   Unprotected: No input validation was used, making these fields more vulnerable to SQL injection.

   Protected: Input sanitization techniques [323] were applied to these areas to remove unique characters or apply remediation

4. System configuration: Insecure Implementation: The application lacked anti-SQL injection, which can directly execute injected SQL code. Secure implementation: This version implemented input sanitization through Python escape functions [324] and parameterized queries to prevent SQL injection attempts.

Table 6 outlines various SQL injection (SQLi) attack types, along with detection and prevention techniques and their estimated effectiveness from 2010 to 2024. Error-based SQLi remains widely recognized, with techniques like detailed error suppression and enhanced logging, achieving a prevention rate of 92-95% when combined with query sanitization and tracking suspicious activity patterns [325]. Union-based SQLi approaches, particularly since 2014, now make use of machine learning for better pattern recognition and routine penetration testing. Object-relational mapping (ORM) frameworks and strict input filtering raise prevention effectiveness to around 90-96% [326].

By 2017, Blind SQLi detection had progressed, with web application firewalls (WAFs) utilizing AI to enhance behavioral analysis and prepared statements, pushing prevention rates to approximately 93-98% [327]. Time-Based Blind SQLi, improved as recently as 2020, now benefits from query time tracking and rate limiting, which limit timing-based attack success and maintain effectiveness rates between 90-94% [328]. Finally, Out-of-Band SQLi, bolstered in 2022, leverages advanced packet inspection, cloud-based intrusion detection, and zero-trust frameworks. These methods enhance API gateway security, achieving an estimated prevention effectiveness between 92-96%.

Table 6. Summary of SQL Injection Attack Types, Detection Techniques, Prevention Methods, and Effectiveness

Type of SQL Injection	Year	Detection Techniques	Prevention Techniques	Effectiveness (%)	Empirical Data
Error-Based SQLi (Modern)	2010	AI-Enhanced Error Analysis; Advanced Logging Systems	Dynamic Query Sanitization; AI-Driven Anomaly Detection	92-95%	AI-based detection systems have reduced SQLi incidents by up to 93%.
Union-Based SQLi (Advanced)	2014	ML-Based Pattern Recognition; Frequent Red Teaming	ORMs with Enhanced Validation; Context-Aware Input Filtering	90-96%	Studies show a 95% reduction with ORMs and advanced filtering mechanisms.
Blind SQLi (Modern)	2017	Behavioral AI; Enhanced WAFs with ML	Prepared Statements; Context-Aware Firewall Configurations	93-98%	ML-optimized WAFs identified up to 97% of blind SQLi attempts.
Time-Based Blind SQLi (Updated)	2020	Time Series Analysis; AI Detection of Latency Anomalies	Rate Limiting & Query Time Monitoring; Delayed Responses	90-94%	Rate limiting and latency-based detection reduced success rates by 90%.
Out-of-Band SQLi (Current)	2022	Deep Packet Inspection; Cloud-Based Intrusion Detection Systems (IDS)	Strict API Gateway Policies; Zero Trust Access Control	92-96%	Cloud-based IDS solutions and Zero Trust models reduced out-of-band attacks by 92%.

Insecure Login Implementation (Detection of SQL Injection)

As part of our exploration into the security vulnerabilities present in edge computing environments, we carried out a practical simulation of an SQL injection assault [329]. This sort of assault is one of the maximum not unusual and threatening strategies utilized by attackers to compromise internet programs. By performing SQL injection, our goal was to demonstrate how effortless vulnerabilities in input validation can lead to unauthorized access and log violations. The following sections describe in detail the design of our simulation, the specific access system, and the resulting capabilities when such attacks are successfully handled. This real-world example highlights the important desire for stronger security measures as opposed to SQL injection and to protect against comparable threats.

Overview of SQL Injection

SQL injection is one of the most common and dangerous vulnerabilities found in web applications. This happens when an attacker can enter or “use” a query that contains malicious SQL code, which is then executed by an external database. Such attacks allow someone to gain access to sensitive data, modify the data, or even take complete control of the database server.

Typically, SQL injection [330] results in useless input validation and proper sanitation in web forms or URL parameters. When a web application adds user information directly to SQL queries without adequate security, it is vulnerable. For example, a particular login form that passes a user’s input directly into an SQL query may allow an attacker to add additional SQL commands.

In its basic form, an attacker could put something like “’ OR ’1’=’1” in the entry field. If the application does not prepare this input, the resulting SQL query can always evaluate it as valid, providing access without the need for a valid password. More advanced SQL injection techniques [331] can be used to retrieve data from tables, perform business operations, or recover corrupted data.

The consequences of SQL injection attacks can be severe. An attacker can remove all data from the database, change or delete data, or even increase their access to gain control of the entire web server. High-profile breaches involving SQL injection have resulted in massive data loss, financial ruin, and reputational damage to organizations.

Regularly updating and updating the database management system and application in addition to using robust input validation to reduce SQL injections [332], using parameterized queries (prepared statements), and security best practices as a minimum privileged access to databases.

The SQL injection (SQLi) attack we implemented is an authentication bypass attack [333]. This type of attack exploits vulnerabilities in login forms, allowing unauthorized access by manipulating the SQL queries that validate user credentials. In our case, we created a simulated login process, where an attacker could attempt to input malicious SQL statements in the login fields to bypass authentication checks. In the upcoming section, we demonstrated how process has been implemented.

Simulation of SQL Injection Attack

To illustrate the SQL injection attack, we created a basic web application on an edge computing network with a login interface. The application included two input fields: Username and Password. The user is required to enter their credentials into these fields and then click the Login button.

Username Field: Users input their username.

Password Field: Users input their password.

When the user clicks the Login button, the application performs validation on the entered credentials. This validation is executed on the back end, where the input values are checked against stored records in the database [334].

Normal Login Process

When a legitimate user enters valid credentials, the back-end verifies these credentials against the database. If the credentials match a valid user record, the application grants access and displays a “Welcome Admin” message on the screen.

[See PDF for image]

Fig. 12

Secure Login Page

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Fig. 13

Authenticated web page

Fig.12 and Fig.13 demonstrate a successful login process where valid credentials (“admin” as the username and “password123” as the password) were provided, you can see it in Fig.12. The system correctly identifies these credentials as genuine and grants access to the admin user, redirecting them to a page that welcomes them as “admin”, refer Fig.13.

Handling Invalid Credentials

If a user enters incorrect credentials, the application prompts an error message, such as “Invalid Credentials.” This error message indicates that the username or password does not match any records in the database.

[See PDF for image]

Fig. 14

Insecure Login Page

Fig.14 shows the system is secured, where if an attacker attempts to guess the username and password, the application should prevent unauthorized access by returning this error message.

SQL Injection Attack Scenario

In cases where an SQL injection attack is attempted, the attacker might input malicious SQL code into the username or password fields. For example, the attacker could enter a SQL query that tricks the back-end into validating their input as legitimate, even when the credentials are incorrect or absent.

If the web application does not properly sanitize user inputs, the back-end [335] may execute the injected SQL code, leading to unauthorized access. In our simulation, when an SQL injection is successful, the attacker is granted access, and the application displays a “Welcome Hacker” message on the screen.

[See PDF for image]

Fig. 15

Hacker login

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Fig. 16

SQL injection detected

Figure 15: This figure shows a login attempt where the user inputs incorrect credentials combined with an SQL injection attempt (’ OR ’1’=’1). The system recognizes the input as an SQL injection and redirects the user to an unauthorized page with the message “Welcome, Hacker!” The unauthorized page is shown in the next figure, Figure 16.

These two images together demonstrate that the system successfully detected the SQL injection attack.

To further illustrate the implementation of this process, the following algorithm details the steps involved in simulating the login functionality and detecting SQL injection attempts within a Flask web application [336]. It initializes by creating an application instance and defining a simulated database D that stores valid username-password pairs. The login page is then rendered, allowing users to input their credentials. Upon form submission, the algorithm extracts the entered username U and password P from the request object R. It then checks whether the credentials match those stored in the database D. If the credentials are valid, a welcome message is returned; otherwise, an error message is displayed. This algorithm also serves as a foundation for demonstrating how SQL injection attacks can be detected when invalid or malicious inputs are processed.

Notations

• $U$: Username

• $P$: Password

• $D$: Dictionary storing valid usernames and passwords

• $R$: Request object containing user input

• $S$: Server response

Algorithm Steps

1. Initialization:

   • A Flask application instance is created.

   • A simulated database $D$ is defined, containing key-value pairs that represent valid usernames and corresponding passwords: $$\begin{array}{c}\hfill D=\{(U_1,P_1),(U_2,P_2),\dots ,(U_n,P_n)\}\end{array}$$

2. Rendering the Login Page:

   • An HTML template for the login page is defined, which includes input fields for $U$ and $P$, with the password field displayed as visible text for testing purposes.

   • The login form is then displayed to the user.

3. Handling Login Submission:

   • A route is defined to handle POST requests to the root URL.

   • Upon receiving a form submission:

     • $U$ and $P$ are extracted from $R$ using the following assignments: $$\begin{array}{c}\hfill U\leftarrow R.\text{form}{[}^{\prime }usernam{e}^{\prime }],P\leftarrow R.\text{form}{[}^{\prime }passwor{d}^{\prime }]\end{array}$$

     • The entered credentials are printed for debugging.

     • If $U$ exists in $D$:

       • The entered password P is compared with the stored password for U in $D$: $$\begin{array}{c}\hfill \text{if}D[U]=P\text{then}\end{array}$$

       • If the credentials match, a success message $S$ is returned: $$\begin{array}{c}\hfill S\leftarrow ``\text{Welcome,}_{}^{\prime \prime }+U\end{array}$$

       • Otherwise, an error message $S$ is returned: $$\begin{array}{c}\hfill S\leftarrow ``\text{Invalid}{\text{credentials!}}^{\prime \prime }\end{array}$$

     • If $U$ does not exist in $D$, an error message $S$ is returned: $$\begin{array}{c}\hfill S\leftarrow ``\text{Invalid}{\text{credentials!}}^{\prime \prime }\end{array}$$

Execution:

• The Flask application is run in debug mode.

Sample of an SQL injection when malicious input is passed through unsanitized queries is discussed below.

Fig. 18 showcases the process of a user accessing a database through an SQL query. In a vulnerable system, where input is not properly sanitized, this query can be manipulated by an attacker using SQL injection. For example, if an attacker submits a malicious input like ’ OR ’1’=’1, the system might construct a query as shown in Fig. 17.

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Fig. 17

SQL Query

This condition (’1’=’1’) is always true, which tricks the system into bypassing the authentication checks. As a result, the attacker gains unauthorized access to the database, potentially exposing sensitive data such as user credentials or confidential records. In this scenario, the hacker can manipulate or extract data, leading to severe security breaches, data theft, or even control over the entire system.

[See PDF for image]

Fig. 18

Accessing database with sql injection

Secure Login Implementation (Prevention of SQL Injection)

After discovering SQL injection vulnerabilities in our simulated web utility, we carried out strategies to prevent such assaults. The important technique used was back-end input sanitation, which ensured that any person’s input became well-proven and sanitized before being processed by the application.

Input Sanitization

To prevent SQL injection attacks, we added an input sanitizer to the back-end of our web application. The sanitizer works by examining and cleaning the data entered in the Username and Password fields before it is used in any SQL query [337]. This function removes or disables any potentially malicious SQL code that an attacker may attempt to inject.

For example, if an attacker tries to login by entering the username as administrator and the password ’ OR ’1’=’1, the input sanitizer detects and neutralizes the malicious input work. As a result, the back-end does not execute the embedded SQL code but a regular input string it behaves like.

If any attempt to pass the login system through SQL injection is detected, the device responds by way of showing an “invalid credentials” mistake message on the login web page. This prevents unauthorized right of entry and does now not crash the machine. After coming across SQL injection vulnerabilities in our simulated internet application, we applied strategies to save you from such attacks. The principal method used turned into back-end enter sanitation, which ensured that any person entered turned into well well-proven and cleaned up earlier than being processed utilizing the software.

[See PDF for image]

Fig. 19

SQL injection prevention

Fig.19 illustrates the effectiveness of the input sanitation process. Even when an attacker attempts to manipulate the input fields with SQL [338] injection, the application maintains security by blocking the attack and denying access.

To demonstrate a similar implementation of the SQL injection prevention policy [339], the following rule set creates the relevant steps to emulate login operations in the Flask web utility and stop the SQL injection attempt process from starting Flask an example of the utility is defined as a simulated database D that stores of valid username-password pairs. The login page is rendered, permitting users to input their credentials. Upon form submission, the set of rules extracts the entered username U and password P from the request object R. The entry is then sanitized to put off or neutralize any doubtlessly harmful SQL code. If the sanitized credentials stored in the database are healthy D, then the welcome message is again; In another case, an error message is displayed. This algorithm demonstrates how SQL injection attacks can be effectively prevented by input processing through a sanitation layer, ensuring that even invalid or malicious inputs no longer compromise the security of the utility.

Notations

• $U$: Username

• $P$: Password

• $D$: Dictionary storing valid usernames and passwords

• $R$: Request object containing user input

• $S$: Server response

Algorithm Steps

1. Initialization:

   • A Flask application instance is created.

   • A simulated database $D$ is defined, consisting of key-value pairs that represent valid usernames and corresponding passwords: $$\begin{array}{c}\hfill D=\{(U_1,P_1),(U_2,P_2),\dots ,(U_n,P_n)\}\end{array}$$

2. Rendering the Login Page:

   • An HTML template for the login page is defined, which includes input fields for $U$ and $P$, with the password field displayed as visible text for testing purposes.

   • The login form is then displayed to the user.

3. Handling Login Submission:

   • A route is defined to handle POST requests to the root URL.

   • Upon receiving a form submission:

     • $U$ and $P$ are extracted from $R$ using the following assignments: $$\begin{array}{c}\hfill U\leftarrow R.\text{form}{[}^{\prime }usernam{e}^{\prime }],P\leftarrow R.\text{form}{[}^{\prime }passwor{d}^{\prime }]\end{array}$$

     • The entered credentials are printed for debugging.

     • If $U$ exists in $D$:

       • The entered password $P$ is compared with the stored password for $U$ in $D$: $$\begin{array}{c}\hfill \text{if}D[U]=P\text{then}\end{array}$$

       • If the credentials match, a success message $S$ is returned: $$\begin{array}{c}\hfill S\leftarrow ``\text{Welcome,}_{}^{\prime \prime }+U\end{array}$$

       • Otherwise, an error message $S$ is returned: $$\begin{array}{c}\hfill S\leftarrow ``\text{Invalid}{\text{credentials!}}^{\prime \prime }\end{array}$$

     • If $U$ does not exist in $D$, an error message $S$ is returned: $$\begin{array}{c}\hfill S\leftarrow ``\text{Invalid}{\text{credentials!}}^{\prime \prime }\end{array}$$

SQL Injection Prevention:

• User inputs are securely handled by directly comparing them against the stored dictionary values, thereby preventing the construction of dynamic SQL queries, which mitigates the risk of SQL injection attacks.

Execution:

• The Flask application is run in debug mode.

Key Differences Between Secured and Unsecured Implementation

In this section, we present an overview of the differences between a secured and unsecured implementation of a web application.

• Insecure Implementation:

  • The algorithm does not implement secure input validation, leaving the application vulnerable to SQL injection attacks.

  • The password field is displayed as plain text to facilitate testing.

• Secure Implementation:

  • The algorithm employs secure input validation by directly comparing user input against stored values without constructing dynamic SQL queries [340].

  • This approach ensures that SQL injection attacks are effectively prevented.

Key Evaluation Metrics

These tests will follow several key performance specifications:

• SQL Injection Detection Rate [341]: Our goal is to measure the success of input sanitization in detecting and blocking SQL injection attempts in the secure version, compared to the insecure version.

• Response time [342]: The application’s processing time for both legitimate and malicious inputs will be recorded. Because of the input sanitization procedures, we anticipate a modest performance overhead in the secured implementation.

• Preventing unauthorized access [343]: In all versions, the quantity of successful illegal logins or attempts at data recovery will be monitored. SQL injection attacks are expected to overcome authentication methods [344] in the insecure version, whereas they should be stopped in the secured version.

• Error handling: We will look at how both versions handle errors, especially in response to SQL injection attempts [345]. The insecure version may return a detailed error message that reveals sensitive information, while the insecure version may return a generic error message to prevent the display of information.

These studies are planned as part of future work, while establishing an experimental environment and simulating attacks has been completed. Based on the stated metrics, data collection and analysis will follow. These results provide insight into how effective input sanitization is in preventing SQL injection attacks in real-world applications.

Table 7. Comparison of SQL Injection Metrics between Your Web Application and General Results

Metric	Our Web Application Results	General Results
SQL Injection Detection Rate	90-95% (High)	75-95% (Varies with tool/technique)
Response Time	Instantaneous ( 1 second)	Milliseconds to 1 second
Preventing Unauthorized Access	98% (Very High with Sanitization)	90-99% (High with Prepared Statements)
Error Handling	Error masked, proper logging	Good practice: Error masking and logging

Table 7 presents a comparison of key SQL injection metrics derived from our web application simulation against typical industry results. The SQL injection detection rate [346] for our application reached 90-95%, outperforming the general range of 75-95%. Response time was notably fast, averaging 1 second, consistent with general expectations of milliseconds to 1 second. Our system achieved a 98% success rate in preventing unauthorized access, aligning with best practices, which typically range from 90-99%. Finally, effective error handling ensured that SQL-related errors were masked, adhering to industry standards while facilitating internal logging for improved security analysis.

Challenges in Simulating SQL Injection for Login Authentication

Simulating SQL injection vulnerabilities within a managed environment presents unique and demanding situations, especially while limited to a single-page web application with login capability. The purpose of this simulation is to successfully show the risks of insecure login implementations and the measures needed to stand in opposition to SQL injection assaults [347]. However, growing a susceptible yet sensible setup for academic purposes involves balancing safety with intentional weaknesses. Additionally, the simulation needs to capture distinctive injection techniques within the restrained scope of login shape inputs, providing a clear illustration of both success and blocked attacks. This section outlines the primary obstacles faced during the simulation process.

Simulating Secure and Vulnerable Login Mechanisms: Creating a susceptible and stable login machine to demonstrate SQL injection risks [348] and mitigation processes requires balancing realism with managed flaws. The prone login has to take delivery of inputs that resemble recognized SQL injection assaults, allowing an unauthorized right of entry. Meanwhile, the stable version has to definitely block these inputs via parameterized queries or organized statements. Balancing those implementations takes cautious training to make sure that every model serves its features without introducing undesirable dangers.

Crafting Realistic SQL Injection Inputs: Developing input styles that realistically mimic SQL injection assaults [349] while remaining safe in a managed environment can be challenging. Attackers often use particular characters and logical situations (e.g., - -, ’; DROP TABLE, OR 1=1) to control SQL queries. Ensuring those inputs gain unauthorized access [350] within the simple login form requires a balance between complexity and readability for demonstration purposes, while it should reflect actual real-world attack vectors.

Testing for Edge Case Inputs: An essential part of SQL injection simulation is ensuring the login form responds correctly to edge case inputs, like malformed or logic-based injections, which attackers might use. Testing various scenarios, from special characters to unexpected SQL logic [351], requires detailed configuration and extensive testing. Properly handling these cases highlights the need for robust input validation and demonstrates how each injection method interacts with both the vulnerable and secure login setups.

Authentication Bypass Observations: A key goal in the simulation is to show how specific SQL injection patterns can bypass authentication [352] to gain unauthorized access. With only a login page, providing clear indicators of success or failure can be challenging. Visual or textual feedback, such as showing access status or recording attempts, helps highlight when injections succeed or are blocked. This contrast between the secure and vulnerable versions emphasizes the importance of SQL injection prevention in real-world applications.

Conclusion and Future Scope

This review provides a contemporary context of cybersecurity, focusing on types of attacks, detection methods, and prevention strategies. We discuss real-time attacks using the Mirai Botnet [353] and VPN Filter, partly exposing vulnerabilities in computing environments. Through our exploration of detection and prevention strategies, we recognized numerous demanding situations that continue to be in securing these networks.

In our simulation, we tested how a simple net utility can be used to hit upon and prevent SQL injection assaults [354], a not-unusual risk in edge computing. This realistic instance underscores the importance of robust enter validation and back-end security features to mitigate such dangers.

While our paper covers some of the most important material pathways for detection and prevention, it is not clear how many other pathways and approaches exist that could be explored. Edge computing is a rapidly growing industry, and the threat continues to evolve.

Moreover, as edge networks grow in complexity, incorporating adaptive, AI-driven [355] security strategies holds promise for boosting real-time threat detection and response. With non-stop development in those regions, edge computing can hold its promise of low-latency, localized processing while ensuring strong safety toward emerging security challenges.

In conclusion, securing edge computing networks is a critical challenge that requires ongoing research and innovation. By leveraging the right tools and approaches, we can continue to improve the security posture of these systems and protect them from increasingly sophisticated attacks.

Future Work and Impact Expected Although all the assessments and evaluation of metrics have not yet been completed, we hope that the findings will highlight the necessity of strong input validation in protecting web applications from SQL injection attacks [356]. The findings and the metrics are likely to contribute to the larger field of edge computing security by emphasizing the vulnerabilities that occur when input sanitization is not performed, as well as the effectiveness of such procedures in lowering attack success rates.

This work lays a foundation for further research focused on strengthening security within distributed and edge computing environments. Future efforts can expand the findings in the current work by utilizing advanced simulation tools, such as iFogSim [357] and CloudSim, to model and evaluate security measures under varied edge network conditions. These platforms will enable researchers to simulate complex, large-scale attack scenarios in realistic edge architectures, providing critical insights into the resilience of different security protocols across geographically distributed nodes [358].

In addition, integrating adaptive security systems driven by AI and machine learning [359] could enable more intelligent, context-aware responses to evolving threats, aligning with the high demands for real-time threat mitigation in edge environments. Further research could also focus on developing standardized security frameworks tailored specifically for edge computing, promoting consistency and best practices across devices, applications, and platforms in this sector.

Overall, the anticipated contributions of this work are twofold: first, to advance the practical security of edge networks, and second, to encourage the industry to adopt adaptive, simulation-based, and standardized security approaches [360]. These efforts will not only enhance the immediate security of edge computing networks but also pave the way for a more resilient, adaptive, and cohesive defense framework against increasingly sophisticated cyber threats in the field of distributed computing.

Author Contributions

Not Applicable

Funding Information

Not Applicable

Data Availability Statement

Not Applicable

Declarations

Conflict of interests

Not Applicable

Research Involving Human and /or Animals

Not Applicable

Informed Consent

Not Applicable

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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