A Survey on Edge Computing (EC) Security Challenges: Classification, Threats, and Mitigation Strategies

Abstract

Edge computing (EC) is a distributed computing approach to processing data at the network edge, either by the device or a local server, instead of centralized data centers or the cloud. EC proximity to the data source can provide faster insights, response time, and bandwidth utilization. However, the distributed architecture of EC makes it vulnerable to data security breaches and diverse attack vectors. The edge paradigm has limited availability of resources like memory and battery power. Also, the heterogeneous nature of the hardware, diverse communication protocols, and difficulty in timely updating security patches exist. A significant number of researchers have presented countermeasures for the detection and mitigation of data security threats in an EC paradigm. However, an approach that differs from traditional data security and privacy-preserving mechanisms already used in cloud computing is required. Artificial Intelligence (AI) greatly improves EC security through advanced threat detection, automated responses, and optimized resource management. When combined with Physical Unclonable Functions (PUFs), AI further strengthens data security by leveraging PUFs’ unique and unclonable attributes alongside AI’s adaptive and efficient management features. This paper investigates various edge security strategies and cutting-edge solutions. It presents a comparison between existing strategies, highlighting their benefits and limitations. Additionally, the paper offers a detailed discussion of EC security threats, including their characteristics and the classification of different attack types. The paper also provides an overview of the security and privacy needs of the EC, detailing the technological methods employed to address threats. Its goal is to assist future researchers in pinpointing potential research opportunities.