1. Introduction
The Internet is rapidly gaining a footing and affecting every aspect of life. Through the Internet, everyone can access information at any time and from any location [1]. In daily life, web applications are becoming more common. The ever-increasing reliance on web applications for various aspects of our lives, from online banking to social media, necessitates robust security measures [2]. Cyber protection has become increasingly vital in safeguarding sensitive information stored within web browsers, including user account credentials [3]. Despite efforts to secure web applications, they remain vulnerable to hacking due to inherent vulnerabilities, particularly during the initial deployment phases [4]. The Open Web Application Security Project (OWASP) regularly assesses these vulnerabilities, gathering data from over 200,000 organizations and business professionals [5]. Based on the OWASP, the most common vulnerabilities in web applications are injection attacks like cross-site scripting (XSS) and Structured Query Language (SQL) injection and demand significant attention from researchers [6,7]. Moreover, the staggering global costs of threat operations, exceeding USD 6 trillion by the end of 2021 [8], underscore the urgency of addressing such vulnerabilities.
Every vulnerability offers a different entry point for cyber threats: attackers can change databases using SQL injection and insert malicious scripts into web pages using XSS. EdgeScan’s 2023 vulnerability statistics report is shown in Figure 1. It illustrates the most common critical vulnerabilities, where XSS attacks take over 19.10% [9].
Web application security focuses on identifying and addressing security vulnerabilities at the web application level, along with implementing effective solutions for these flaws [10]. Web security is essential for businesses because websites and web servers are vulnerable to internal and external threats. Strict policy measures must be implemented to avoid manipulation or unwanted access to sensitive data or destruction, which could harm the company’s operations or reputation. Online security principles include authentication, authorization, auditing, and logging [11]. Security is essential for a secure web application. State integrity refers to maintaining the application’s state, which should be kept untampered; logic correctness means that the logic of the application should be precisely corrected as intended by the developers; input validity means that user input is verified before it is used; and security misconfiguration refers to configuration settings and using secure components. For online applications to guarantee the authenticity and responsiveness of user inputs, input validation is essential. Both client-side and server-side inputs, such as HTML5, post message invocations, POST method, database queries, and HTTP request query strings, should be subject to validation [5].
XSS represents a pervasive threat in the realm of cybersecurity, characterized as a client-side code injection attack that exploits vulnerabilities in both client-side and server-side components of web applications [12]. In such attacks, attackers leverage resources from third-party websites to launch scripts within the victim’s browser environment. Typically, attackers directly insert payloads containing Java Script (JS) into the database of a targeted website. Subsequently, when a user requests a page from the compromised website, the malicious script-containing page is delivered to the victim’s browser, wherein the attacker’s payload is executed as part of the Hypertext Markup Language (HTML) body. This method allows attackers to manipulate user interactions, steal sensitive information, or compromise the integrity of web applications [8]. Furthermore, attackers exploit vulnerabilities that are publicly disclosed before patches are developed and deployed, enabling unauthorized access to systems and the unauthorized alteration or theft of data [13]. Figure 2 illustrates the general XSS attacks.
By inserting additional HTML or client script code into a website or input form, attackers can compromise the security of users’ browsers, gaining unauthorized access to sensitive data such as cookies, session tokens, etc. [12,14]. This malicious script, capable of rewriting HTML text, enables attackers to compromise user security, extract sensitive data, or even deploy harmful software [15].
Figure 3 illustrates the two primary types of XSS vulnerabilities [15,16]:
Client-side (Document Object Model (DOM)-based XSS);
Server-side (persistent and non-persistent XSS).
In a stored or persistent XSS attack, malicious scripts are injected into a website, with blogs and forums often being the targets [17]. A database on the website is used to hold scripts that hackers input through forms, posts, or comments. When a victim accesses the compromised website, the script runs, leading the victim to a malicious database. Data belonging to the victim can now be accessed by hackers. Via embedding malicious code that only becomes active when the user visits the infected page, the attack compromises the user’s private information by taking advantage of different syntactic symbols [8].
The way a reflected or non-persistent XSS attack works is that a malicious URL is embedded on a website by the attacker, and the user clicks on it. The website poses as a regular browser but really takes the visitor to a malicious page. When the XSS script is parsed by the user, requests are sent to the malicious server. After that, hackers ask the victim to provide their information, which they then take [18].
In DOM-based XSS attacks, the malicious script is loaded into the web browser’s DOM, allowing injected code to modify the DOM’s contents and an object’s properties any time a victim visits a susceptible website [8]. This vulnerability is exploited using client-side scripting, including JavaScript, VBScript, AJAX, ActiveX, and JQuery. AJAX queries and image tags can be used by DOM-XSS attackers to target third-party apps, contrary to the common misconception that they improve online security [19]. Attackers are able to obtain cookies and confidential information such as IP addresses and user passwords as a result [8]. DOM-based XSS attacks alter the client’s DOM directly as opposed to reflected XSS attacks, which include introducing malicious code into the website’s response [17].
Server-side XSS vulnerabilities arise when unsensitized data from the server are incorporated directly into the HTTP response, posing significant risks to all users accessing the compromised website. Conversely, client-side XSS vulnerabilities emerge when JS manipulates page content, allowing attackers to craft malicious Uniform Resource Locators (URLs) containing unfiltered text that executes upon user interaction. The repercussions of successful XSS payloads include cookie theft, keylogging, session hijacking, and identity theft [16].
Despite the widespread adoption of standardized code development practices, over 60% of websites remain vulnerable to XSS attacks, highlighting the critical need for robust detection and prevention mechanisms [20]. Identifying and thwarting XSS attacks are paramount for safeguarding web applications and protecting sensitive user data. To this end, various analysis techniques have been employed, including static analysis, dynamic analysis, and machine learning (ML). Static analysis involves scrutinizing the source code to detect vulnerabilities, offering assurances regarding specific vulnerability absence but potentially requiring extensive time and yielding limited results [21]. Conversely, dynamic analysis focuses on understanding script behavior during execution, facilitating the identification of unknown vulnerabilities and novel attack types that static analysis may overlook [21].
Traditional methods for XSS detection often suffer from high false-positive (FP) rates, meaning that they flag harmless activity as malicious. Additionally, these methods may struggle to adapt to new attack vectors employed by cybercriminals. ML, on the other hand, leverages existing script data to create classifiers and predict the behavior of new scripts, offering the rapid identification of malicious scenarios, adaptability to evolving attack types, and the ability to operate across diverse application environments without the need for a dedicated analysis environment [21].
The integration of ML into XSS detection frameworks holds significant promise, enabling enhanced threat detection capabilities and proactive defense measures against evolving cyber threats. This paper explores the effectiveness and benefits of ML-based XSS detection methods, highlighting their potential to bolster web application security and mitigate the risk of XSS attacks.
This research has several key objectives:
Create an ML-based model: We provide an ML model that greatly enhances the precision and potency of XSS detection in web applications.
Identify ideal features: To guarantee the accurate detection of XSS attacks while reducing false alarms, we seek to identify the best traits and data sources for ML model training.
Assess the efficacy of ML-based detection systems: We will evaluate the ML-based approach’s accuracy, efficiency, and reliability by comparing it with state-of-the-art detection techniques.
Examine current methods: We will quickly summarize current ML and deep learning (DL) algorithms that have been applied to XSS detection.
This research addresses these challenges by proposing a novel ML-based system for XSS attack detection. Our aim is to enhance web application security by developing a more accurate, robust, and adaptable detection system. By reducing the number of FPs and effectively identifying XSS attacks, this research contributes significantly to the improvement in web application security practices.
We develop and assess the effectiveness of ML-based methods, including Decision Trees (DTs), Support Vector Machines (SVMs), Random Forest (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and ensemble learning with feature selection techniques such as Information Gain (IG) and Analysis of Variance (ANOVA) to identify the most relevant features and enhance the accuracy and efficiency of identifying XSS in web applications. The experiment is conducted using the XSS dataset that was published by Mokbal et al. [22]. We selected the top 25 features using IG as a feature selection technique to choose the most informative features.
After a comparative analysis of the ten models’ performance, the RF, ensemble model of RF, DTs with Gradient Boost (GB), and ensemble mode of RF with MLP, respectively, scored 99.78%, 99.76%, and 99.65% in terms of accuracy and achieved high results in the other evaluation metrics. The proposed models provide significant performance improvements compared to other existing state-of-the-art methods as they can detect XSS-based attacks while simultaneously minimizing FP and false-negative (FN) rates. Moreover, the proposed method is compared with other existing state-of-the-art XSS attack detection methods.
The remainder of this paper is organized as follows. Section 2 presents different methods for XSS attack detection, followed by the proposed research methodology in Section 3. Section 4 and Section 5 present the results and discussion, respectively. Finally, Section 6 contains the conclusion.
2. Related Work
ML approaches have emerged as a promising avenue for XSS attack detection in web applications. Their ability to learn from data and adapt to evolving attack patterns offers significant advantages over traditional methods [23]. However, selecting the most effective preprocessing techniques is crucial to optimize detection performance [23].
Several studies have explored various ML algorithms for XSS detection. Banerjee et al. [24] implemented ML algorithms for identifying XSS threats. These algorithms include SVMs, KNN, RF, and LR. The LR model was utilized to map the true and false values included in the dataset. The implementation of the suggested model used a dataset with 24 attributes based on JS and URL features. Among these, 1453 samples were benign, while 158 were flagged as malicious. They achieved the highest accuracy of 98% for the RF classifier. Similarly, Habibi and Surantha [12] proposed a method to enhance XSS attack detection performance by using different ML techniques with an n-gram approach to script features. These techniques include SVMs, KNN, and NB. The results demonstrate that SVMs and the n-gram method work together to reach the maximum accuracy and achieve an accuracy of 98%. Kascheev and Olenchikova [21] compared various ML algorithms such as the SVMs, DTs, NB, and LR classifier, with DTs achieving the highest accuracy of 98.81%. While these studies demonstrate the effectiveness of ML, they also highlight the importance of feature selection, as evidenced by the lower performance of LR in Kascheev and Olenchikova’s work [21].
Gogoi et al. [25] proposed a hybrid approach combining traditional Web Application Firewalls with ML algorithms like SVMs. Their focus on reducing FPs and FNs while maintaining high precision yielded promising results of 98%. They discovered that SVMs successfully distinguished inputs from XSS attacks and legitimate web applications with a balance between precision and accuracy. Mokbal et al. [22] introduced the XGBXSS framework, utilizing XGBoost and a hybrid feature selection technique consisting of sequential backward selection (SBS) combined with Information Gain (IG) to choose an optimal subset while lowering computing expenses and preserving the good performance of the detector. Also, the study’s dataset included 138,569 samples, with 100,000 samples classified as benign. This ensemble learning approach achieved exceptional performance with an accuracy of 99.59%, precision of 99.53%, and a low FP rate of 0.18%.
Research on hybrid models for XSS detection is also gaining traction. Stiawan et al. [26] combined Long Short-Term Memory (LSTM) with Principal Component Analysis (PCA) for dimensionality reduction, achieving 96.85% accuracy. Other studies explored combinations of LSTM and CNNs [27] or CNNs and scanners [28] to achieve high accuracy rates exceeding 99%. Melicher et al. [29] proposed a method integrating three-layer Deep Neural Networks (DNN) with taint tracking for DOM XSS detection, achieving 95% accuracy but with limitations in precision, achieving 26.7%. Alaoui et al. [30] utilized an LSTM Encoder–Decoder with word embeddings including tools like word2vec, FastText, and Glove for XSS attack detection, reaching a precision of 99.09% and recall and accuracy of 99.08% each.
Gupta et al. [31] presented GeneMiner, a system for detecting novel XSS attacks. It consists of GeneMiner-E for extracting new features and GeneMiner-C for classifying input payloads as either malicious or benign. GeneMiner responds to changing patterns of attack payloads to detect adversarial XSS attacks. They evaluate their classification accuracy by comparing it with NB, RF, LR, SVMs, AdaBoost, MLP, CNNs, and reinforcement learning. Their approach achieved an accuracy of 98.5% in identifying newly crafted malicious payloads. Dawadi et al. [32] conducted a comparative analysis using LSTMs for Distributed Denial of Service (DDoS), XSS, and SQL injection detection, achieving an accuracy of 89.34% for XSS attacks within their layered architecture model.
The relevance of XSS attack detection extends beyond web applications to Internet of Things (IoT) devices and cloud-based services. Tian et al. [33] proposed a CNN-based method for edge devices that can be used with cloud-based web applications. The suggested model used the Continuous Bag of Words (CBOWs) model to vectorize URLs during the data preparation phase. The Rectified Linear Unit (ReLU) function, dropout layers, and pooling layers are used in the CNN architecture to optimize the CNN model, achieving an accuracy of 99.41%. Chaudhary et al. [34] introduced a CNN-based approach for identifying XSS attacks. The suggested approach applies CNNs after two stages of data preparation, specifically decoding and contextual tagging. Their work has been implemented in Fog nodes connected with IoT networks, achieving an accuracy of 99.88%. Luo et al. [35] proposed an Ensemble DL-Based Web Attack Detection System (EDL-WADS) for online attack detection in IoT networks. The suggested model examined URL requests for abnormalities, and it used a combination of three DL-based models, namely Multimodal Residual Networks (MRNs), CNNs, and LSTM, achieving 98% accuracy. Finally, Odun-Ayo et al. [36] explored MLP for real-time XSS detection in cloud-based web applications, achieving an accuracy of 99.47%.
Furthermore, several recent studies have introduced novel approaches to detecting XSS attacks, leveraging advanced techniques such as attention mechanisms, generative adversarial networks (GANs), and Monte Carlo Tree Search (MCTS) algorithms to enhance detection accuracy and robustness. For instance, [37] proposed the LSTM-attention detection model, integrating an attention mechanism into the LSTM recurrent neural network (RNN) architecture. Achieving remarkable precision and recall rates of 99.3% and 98.2%, respectively, their method demonstrated superior performance in identifying XSS attacks by enhancing the recognition of malicious codes and feature extraction. In a similar vein, [38] introduced the Paths Attention Method (PATS) for detecting reflected XSS vulnerabilities, utilizing syntactic pathways and attention processes to improve training effectiveness. PATS achieved an accuracy rate of 90.25% and an F1-Score of 81.62% while also addressing dataset limitations through the creation of a reliable dataset consisting of 10,000 benign samples from GitHub and 1000 malicious samples from the National Institute of Standards and Technology (NIST). Additionally, [39] employed the MCTS algorithm and GANs to generate adversarial XSS attacks, enhancing the detection rate of adversarial examples. Their method significantly improved the detector’s performance by increasing the rate of discovering adversarial samples. Meanwhile, [40] emphasized the susceptibility of DL models to adversarial attacks and proposed a GAN-based approach to automatically generate hostile XSS attacks against an LSTM-based XSS attack detection model. It demonstrated a significant decline in the detection model’s performance when tested on modified XSS instances produced by the GAN model and achieved an accuracy of 98%.
Tariq et al. [41] introduced a hybrid methodology combining genetic algorithms, statistical inference, and reinforcement learning (RL) to assess the proximity of each payload to malicious and benign samples, offering a novel approach by merging ML with metaheuristic algorithms like the genetic algorithm, achieving an accuracy of 99.89%. Thajeel et al. [42] addressed the evolving nature of XSS attacks and feature drift using a deep Q-network multi-agent framework (DQN-MAFS) for dynamic feature selection, achieving an accuracy of 98.37%. Their proposed approach, called fair agent reward distribution-based dynamic feature selection (FARD-DFS), outperformed existing methods in terms of accuracy and F1 measure, providing real-time updates and correction of embedded knowledge without the need for offline retraining.
These studies showcase the effectiveness of various ML and DL approaches for XSS attack detection. They highlight the importance of feature selection, ensemble learning techniques, and the exploration of novel architectures like CNNs and LSTMs for achieving high accuracy and adapting to evolving attack vectors. However, challenges such as the lack of standardized datasets, adaptation to emerging attack vectors, and reducing FP rates persist, indicating the need for further research in this field. Our research builds upon this foundation by proposing a novel ML-based model that leverages these insights to further enhance XSS attack detection performance.
3. Research Methodology
This section outlines the methodology employed in conducting research on XSS attack detection utilizing ML techniques and comparison to other state-of-the-art methods. The methodology encompasses data collection, preprocessing, feature selection, model training, and evaluation. Figure 4 illustrates the proposed models’ framework.
3.1. Data Collection
The research utilizes the XSS dataset provided by Mokbal et al. [22]. This dataset comprises 138,569 webpages, with 100,000 labeled as benign and 38,569 labeled as malicious. The benign samples in this dataset were produced using an Alexa ranking of the top 50,000 websites. XSSed and Open Bug Bounty are two examples of raw XSS repositories that crawled to gather malicious samples, ensuring a diverse and representative dataset for analysis. The dataset includes 167 features. Recently, this dataset was made accessible online via GitHub [22]. Additionally, the dataset comprises three distinct feature types: HTML, JS, and URL, and we used the whole dataset in the experiments. Table 1 provides summarized information about the dataset obtained for this research.
3.2. Preprocessing
Before training the models, the dataset undergoes preprocessing to ensure uniformity and relevance. This step involves cleaning the data, handling missing values, class imbalance, and standardizing formats. We handled the class imbalance by upsampling the minority class, which involves increasing the number of samples in the minority class to balance it with the majority class. By dropping duplicates, the XSS dataset is streamlined and ready for subsequent processing steps, including train–test splitting. Furthermore, we used the standard scalar technique in Python language as feature scaling. Moreover, challenges like the presence of irrelevant features are addressed through feature selection techniques.
3.3. Feature Selection
To enhance the accuracy and efficiency of XSS attack detection, feature selection algorithms such as IG and ANOVA are applied. These algorithms help identify the most discriminative and informative features crucial for accurate detection, thereby reducing noise and improving model performance.
When using an ANOVA for feature selection, the variance between feature groups and the variance within groups are compared to obtain the F-values for each feature. High F-value and low p-value features are deemed important and are included in the model. Feature retention is determined by a significance criterion, often set at p < 0.05. This approach increases interpretability, lowers dimensionality, and boosts model performance [43].
IG is a commonly used method to evaluate the importance of features in predicting the target variable (i.e., whether a given input represents a benign or malicious XSS attack). IG measures the reduction in entropy or uncertainty in the target variable that is achieved by splitting the dataset based on the values of a particular feature [44,45].
In the context of using IG and the ANOVA test as feature selection methods for XSS attack detection, the observation that IG yielded better features prompts a discussion about the effectiveness and implications of feature selection techniques. The superiority of IG in selecting features suggests that it successfully identified the most informative predictors for distinguishing between XSS attacks and non-attacks.
By employing IG, the most informative features that contribute significantly to predicting XSS attacks are identified and used to train ML models effectively. This helps improve model performance, reduce computational complexity, and enhance interpretability in XSS detection tasks. Table 2 shows the 25 features selected out of the 167 in the dataset.
3.4. Model Training
This research utilizes a diverse range of ML algorithms during both the training and evaluation phases. These models include DTs, SVMs, RF, LR, XGBoost, MLP, CNNs, and ANNs. Each model is characterized by distinct algorithms and architectures, contributing to the exploration of varied detection methodologies.
In addition to individual models, two ensemble models are investigated: the first ensemble model combines the MLP classifier with RF, while the second integrates DTs with RF with GB. This ensemble approach aims to enhance detection accuracy and robustness by amalgamating the complementary features of constituent algorithms.
The training phase utilizes standard supervised learning approaches provided by the Sklearn library in Python. Each algorithm is trained using 80% of the dataset, randomly selected for model construction, while the remaining 20% is reserved for testing. This partitioning ensures the evaluation of model generalization and performance on unseen data, thereby validating the efficacy of the proposed detection approach.
3.5. Evaluation
The performance of each trained model is evaluated to determine its effectiveness in detecting XSS attacks. Evaluation metrics such as accuracy, precision, recall, F1-Score, Receiver Operating Characteristic—Area Under the Curve (ROC-AUC) score, and confusion matrix are used to assess the models’ performance [23]. The evaluation process provides insights into the strengths and weaknesses of each model, guiding the selection of the most suitable approach for XSS attack detection.
3.5.1. Accuracy
Accuracy is a metric for assessing the potency of a classification model. It shows the proportion of accurately classified samples out of all the samples that have been classified, as shown in Equation (1).
(1)
3.5.2. Precision
Precision serves as a metric for assessing the ability of a model to predict positive samples. The total number of positive samples indicates the ratio of correctly predicted samples (TP), as shown in Equation (2).
(2)
3.5.3. Recall
Recall is a metric that assesses the ability of a model to identify positive samples. The quantity of positive samples that indicate that the prediction was an accurate true positive (TP) divided by the total number of samples in the same real class is what this indicates. It serves as an example of the model’s FN, as shown in Equation (3).
(3)
3.5.4. F1-Score
The F1 measure is sometimes referred to as the harmonic mean of recall and precision because it considers both metrics and provides a fair evaluation of their performance. This assessment metric is commonly used when datasets are uneven, meaning that one class has a much higher number of occurrences than the other, as shown in Equation (4).
(4)
3.5.5. ROC-AUC
ROC-AUC is a popular function in ML for assessing the performance of binary classification models. It computes the ROC-AUC. The ROC-AUC score goes from 0 to 1, with 1 representing perfect classification and 0.5 indicating random guessing. A score higher than 0.5 indicates that the model outperforms random.
As XSS attacks evolve in complexity with advancements in web applications, this research acknowledges the challenges posed by obfuscation techniques and semantic reasoning in attack statements. The methodology is designed to address these challenges by employing robust preprocessing, feature selection, and model training techniques to enhance the detection of XSS vulnerabilities in web applications.
4. Results
The experimental results, as depicted in Table 3, underscore the efficacy of employing diverse ML approaches in XSS attack detection, signifying a significant advancement in web security measures. Moreover, the proposed method is benchmarked against existing state-of-the-art XSS attack detection methods, offering a comparative analysis to gauge its performance and efficacy.
All experiments were meticulously conducted within the Google Colab environment, ensuring consistency and reproducibility in training and testing the models. In the subsequent sections, a detailed exploration of the empirical findings unfolds, shedding light on various aspects, including model performance, feature importance, computational efficiency, and broader implications for bolstering web security against XSS vulnerabilities.
Through this comprehensive analysis, valuable insights are gleaned, paving the way for the development of enhanced detection mechanisms and resilient defense strategies in the dynamic landscape of web security. We applied various ML models, outlined in the following.
4.1. Logistic Regression (LR)
We employed IG to select the top 25 features essential for XSS attack detection. Additionally, we fine-tuned the LR model by specifying a maximum of 1000 iterations. The LR model demonstrated promising performance, achieving an accuracy of 98.28%, with precision and recall rates of 99.38% and 94.32%, respectively. The F1-Score, a harmonic mean of precision and recall, was calculated at 96.79%. Furthermore, the ROC-AUC score, indicative of the model’s ability to distinguish between classes, stood at 97%, highlighting its robustness in discriminating between benign and malicious instances. The confusion matrix provides additional insights into the LR classifier’s performance, revealing a minimal misclassification rate of 0.22% for benign instances and 5.68% for malicious instances.
4.2. Support Vector Machine (SVM)
We utilized IG to select the top 25 features crucial for XSS attack detection. Leveraging the SVC with the Radial Basis Function (RBF) kernel, we set the gamma parameter to ‘scale’ for optimal performance.
The SVM classifier exhibited impressive performance, achieving an accuracy of 98.53%, with precision and recall rates of 99.21% and 97.84%, respectively. The F1-Score, a balanced measure of precision and recall, was calculated at 98.52%. Furthermore, a high ROC-AUC score of 99% underscores the model’s robustness in distinguishing between benign and malicious instances. The confusion matrix provides additional insights into the SVM classifier’s performance, revealing a minimal misclassification rate of 0.78% for benign instances and 2.16% for malicious instances.
4.3. Multi-Layer Perceptron (MLP)
We employed IG to select the top 25 features crucial for XSS attack detection. The MLP classifier, a powerful neural network model, was configured with 100 neurons in the hidden layer, utilizing the ReLU activation function and the Adam optimizer for efficient training.
The MLP classifier demonstrated exceptional performance, achieving an accuracy of 99.14% with precision and recall rates of 99.26% and 99.02%, respectively. The F1-Score, a balanced measure of precision and recall, was calculated at 99.14%, indicating the classifier’s robustness in accurately identifying XSS attacks. Furthermore, a high ROC-AUC score of 99% underscores the model’s ability to discriminate between benign and malicious instances effectively. The confusion matrix provides additional insights, revealing a minimal misclassification rate of 0.73% for benign instances and 0.98% for malicious instances.
4.4. Artificial Neural Networks (ANNs)
We utilized the ANOVA F-test to select the top 25 features essential for detecting XSS attacks. We implemented a sequential model in the ANN architecture, incorporating specific configurations to optimize performance. The model comprises an input layer with the ReLU activation function, followed by two hidden layers with tanh and ReLU activation functions, respectively, and an output layer with sigmoid activation function. Additionally, we employed the Adam optimizer and the binary cross-entropy loss function to facilitate efficient training.
Furthermore, to ensure convergence and robustness, we set the batch size to 32 and trained the model for 50 epochs. The ANN model demonstrates outstanding performance, achieving an accuracy of 99.06%, with precision and recall rates of 99.08% and 99.04%, respectively. The F1-Score, a balanced measure of precision and recall, was calculated at 99.06%, indicating the model’s effectiveness in accurately identifying XSS attacks.
Moreover, a high ROC-AUC score of 99.94% underscores the model’s exceptional ability to discriminate between benign and malicious instances. The confusion matrix provides additional insights, revealing a minimal misclassification rate of 0.92% for benign instances and 0.96% for malicious instances.
4.5. Convolutional Neural Networks (CNNs)
We employed IG to select the top 25 features crucial for detecting XSS attacks. The CNN model was initialized as a sequential model, specifying the number of features. ReLU activation functions were applied to the layers, accompanied by MaxPooling to enhance feature extraction. Additionally, we utilized the Adam optimizer and the binary cross-entropy loss function for efficient training.
The CNN model demonstrates robust performance, achieving an accuracy of 98.82%, with precision and recall rates of 99.57% and 98. 07%, respectively. The F1-Score, a balanced measure of precision and recall, was calculated at 98.81%, indicating the model’s effectiveness in accurately identifying XSS attacks.
Moreover, a high ROC-AUC score of 99.92% underscores the model’s exceptional ability to discriminate between benign and malicious instances. The confusion matrix provides additional insights, revealing a minimal misclassification rate of 0.43% for benign instances and 1.93% for malicious instances.
4.6. Extreme Gradient Boosting (XGBoost)
We employed IG to select the top 25 features crucial for detecting XSS attacks. Furthermore, we meticulously defined the parameters for XGBoost, a renowned gradient-boosting algorithm known for its efficacy in classification tasks. These parameters play a pivotal role in configuring the behavior and performance of the XGBoost model. Specifically, we specified the objective function as binary classification using LR and utilized the classification error rate as the evaluation metric during training. Moreover, we set the maximum depth of each tree to 6, the learning rate to 0.3, and the subsample and colsample_bytree parameters to 1. The evaluation metrics for the XGBoost model on the selected dataset showcased outstanding performance, with an accuracy of 99.62%, precision of 99.70%, recall of 99.54%, and an F1-Score of 99.62%. Notably, an ROC-AUC score of 100% underscores the model’s exceptional ability to distinguish between benign and malicious instances. The confusion matrix provides additional insights, revealing a minimal misclassification rate of 0.30% for benign instances and 0.46% for malicious instances.
4.7. Decision Tree (DT)
We leveraged the top 25 features selected based on IG to train a DT model. We configured the model with a criterion set to “gini” and a minimum samples leaf equal to one to optimize its performance. The DT model exhibited strong performance, achieving an accuracy of 99.47%, with precision and recall rates of 99.22% and 99.72%, respectively. The F1-Score, a balanced measure of precision and recall, was calculated at 99.47%, indicating the model’s effectiveness in accurately identifying XSS attacks. Furthermore, a high ROC-AUC score of 99% underscores the model’s ability to discriminate between benign and malicious instances. The confusion matrix provides additional insights, revealing a minimal misclassification rate of 0.78% for benign instances and 0.28% for malicious instances.
4.8. Random Forest (RF)
We utilized the top 25 features selected through IG to train an RF classifier. We configured the RF model with the n_estimators parameter set to 120 to ensure robustness and accuracy. The RF model demonstrated exceptional performance, achieving an accuracy of 99.78%, with precision and recall rates of 99.80% and 99.75%, respectively. The F1-Score, a balanced measure of precision and recall, was calculated at 99.78%, indicating the model’s effectiveness in accurately identifying XSS attacks.
Moreover, a high ROC-AUC score of 100% underscores the model’s exceptional ability to discriminate between benign and malicious instances. The confusion matrix provides additional insights, revealing a minimal misclassification rate of 0.20% for benign instances and 0.25% for malicious instances.
4.9. Ensemble Model of MLP Classifier and RF
We employed a Voting Classifier that combines the strengths of the MLP classifier and RF to enhance predictive performance. The MLP classifier excels at capturing complex nonlinear relationships within the data, leveraging its layered structure and activation functions to learn intricate patterns and generalize well on unseen data. Conversely, RF is a robust ensemble learning method that aggregates predictions from multiple DTs, offering resistance against overfitting and providing insights into feature importance.
By leveraging the complementary strengths of these models, our ensemble approach aims to improve predictive performance and model generalization across various datasets and applications. We selected the top 25 features using IG. For the RF classifier, we set the n_estimators parameter to 100, while for the MLP classifier, we specified a maximum of 1000 iterations and defined two hidden layers with sizes of 170 and 50 neurons, respectively.
The ensemble model of RF and MLP demonstrates outstanding performance, achieving an accuracy of 99.65%, with precision and recall rates of 99.59% and 99.71%, respectively. The F1-Score, a balanced measure of precision and recall, was calculated at 99.65%, indicating the model’s effectiveness in accurately identifying XSS attacks.
Moreover, a high ROC-AUC score of 100% underscores the model’s exceptional ability to discriminate between benign and malicious instances. The confusion matrix provides additional insights, revealing a minimal misclassification rate of 0.41% for benign instances and 0.29% for malicious instances.
4.10. Ensemble Model of DTs, RF, and GB
We employed a Voting Classifier combining DT and RF with GB in XSS detection attacks. This ensemble technique utilizes hard voting to make final predictions, leveraging the diversity of these algorithms to enhance the overall performance of the XSS detection system.
DTs are powerful models capable of capturing complex relationships through hierarchical decisions, providing interpretability to the ensemble. RF, an ensemble method, aggregates predictions from multiple DTs, reducing overfitting and improving robustness through randomness in the training process. GB constructs an ensemble of weak learners, typically DTs, sequentially focusing on correcting errors, leading to enhanced predictive accuracy.
By combining these three algorithms, we aim to exploit their complementary strengths: DTs for interpretability, RF for robustness, and GB for predictive accuracy. This ensemble approach has the potential to achieve superior performance in detecting XSS attacks by leveraging diversity and ensemble learning techniques. By using a Voting Classifier, we combine the predictions of these classifiers, benefiting from their diversity and leading to a more accurate and reliable detection of XSS attacks.
We selected the top 25 features using IG. For the RF and GB classifiers, we set the n_estimators parameter to 100. The ensemble model of DT and RF with GB demonstrates outstanding performance, achieving an accuracy of 99.76%, with precision and recall rates of 99.74% and 99.77%, respectively. The F1-Score, a balanced measure of precision and recall, was calculated at 99.76%, indicating the model’s effectiveness in accurately identifying XSS attacks. Moreover, a high ROC-AUC score of 100% underscores the model’s exceptional ability to discriminate between benign and malicious instances. The confusion matrix provides additional insights, revealing a minimal misclassification rate of 0.26% for benign instances and 0.23% for malicious instances.
Overall, the ensemble approach combining DT and RF with GB showcases promising results, highlighting its potential as a valuable tool for enhancing web security measures and mitigating cyber threats.
5. Discussion
In summary, the comprehensive evaluation of all the proposed models in our research, as depicted in Figure 5 and Figure 6, underscores the effectiveness of various ML techniques for XSS attack detection. Among these models, the RF model emerges as the top-performing one, exhibiting the highest accuracy and balanced performance across all evaluation metrics. Nevertheless, other models such as XGBoost also demonstrate competitive performance, showcasing the versatility and efficacy of diverse ML approaches in addressing XSS vulnerabilities. The ensemble models, combining multiple classifiers, further accentuate the potential benefits of leveraging ensemble techniques to enhance predictive performance. These findings offer valuable insights for both researchers and practitioners in the cybersecurity domain, guiding the development of more robust and effective XSS detection systems to bolster web security measures and mitigate cyber threats effectively.
5.1. Comparison with Other State-of-the-Art Methods
To verify the performance of our proposed method, we conducted a comparative analysis with several recent XSS attack detection methods, all using the XSS dataset by Mokbal et al. [22]. Mokbal et al. [22] achieved an accuracy of 99.59% using XGBoost with feature selection based on IG and sequential backward selection (SBS), utilizing 30 features. Although their accuracy was commendable, it is noteworthy that our models achieved comparable accuracy with a reduced feature set of 25, highlighting the efficiency of our approach. Thajeel et al. [42] employed a DT model with dynamic selection based on RL, showcasing the potential of dynamic selection techniques. However, their model utilized 167 features and exhibited lower performance compared to ours, suggesting potential limitations in their approach or dataset. Additionally, they re-implemented a combination of genetic algorithm, statistical inference, and RL introduced by Tariq et al. [41] for XSS attack detection.
Our experimental results, as depicted in Table 4 and Figure 7, demonstrate that our proposed methods, along with the method presented by Mokbal et al. [22], achieved the highest accuracy and precision rates. Furthermore, our proposed methods, including RF, ensemble learning models RF and GB with DTs, and MLP with RF, as well as the method from Tariq et al. [41], attained the best recall rates, ranging from 99.54% to 99.77%. These results signify the robustness and effectiveness of our proposed methods in detecting XSS attacks. Among the three proposed models, RF achieved exceptional results in terms of accuracy, precision, and F1-Score; however, in terms of recall, the ensemble learning models RF and GB with DTs achieved higher results. In general, the three proposed models yielded higher results in all evaluation metrics than the existing studies.
Overall, our findings underscore the advancements made in XSS attack detection through ML techniques, emphasizing the importance of algorithm selection and feature engineering in developing accurate and robust detection models. By achieving superior performance compared to existing methods, our research contributes significantly to enhancing web security measures and mitigating cyber threats effectively.
5.2. Practical Implementation Challenges in Real-World Systems
The dynamic nature of XSS attack vectors typically exceeds the capabilities of traditional rule-based detection systems, requiring the adoption of more advanced techniques like ML. However, applying ML models to identify XSS attacks in real-world systems has unique practical challenges. This section delves further into these challenges and offers several ways to deal with them.
It is challenging to collect high-quality labeled data due to data scarcity and unbalanced datasets; hence, crowdsourcing, data augmentation, and synthetic generation of data are used for labeling. Additionally, due to the dynamic nature of XSS attacks, feature engineering has to evolve to include regular updates, the study of user behavior, and advanced techniques such as natural language processing and deep learning.
Moreover, model complexity and performance must be balanced during model training and selection, and overfitting must be controlled via cross-validation and regularization. Furthermore, real-time detection has latency and scalability challenges, which may be addressed by model enhancement, edge computing, and distributed processing. Also, for minimal operational disturbance, modular design, API-based solutions, and gradual deployment are necessary for integrating detection models with current systems.
Additionally, the mitigation of adversarial threats requires ongoing learning and adversarial training. Moreover, managing FP and FN includes modifying judgment thresholds, creating post-processing rules, and incorporating anomaly detection. Finally, it is imperative to handle regulatory and privacy concerns by means of data anonymization, consent management, and frequent audits to ensure compliance with legal frameworks.
Overall, to design successful XSS detection systems, a comprehensive approach that combines technological solutions, ongoing monitoring, and regulatory compliance is necessary.
6. Conclusions
In conclusion, the escalating complexity of XSS attacks underscores the urgency for robust detection mechanisms. As attackers increasingly employ obfuscation techniques to evade detection, traditional methods struggle to keep pace. However, leveraging ML proves to be a potent strategy in combating this evolving threat landscape. ML models offer a more resilient and dynamic protection against malicious code injections because they can learn from enormous volumes of data, adjust to changing attack patterns, and generalize to uncommon circumstances.
This research proposes a unique framework model for XSS attack detection using a comprehensive suite of ML algorithms. Our experimentation encompassed DTs, SVMs, RF, LR, XGboost, MLP, CNNs, ANNs, and ensemble learning techniques. Through rigorous evaluation of a dataset of recent real-world traffic, augmented by feature selection methods like IG and ANOVA, we identified the most effective models.
Among the ten models examined, the RF model emerged as the top performer, achieving an accuracy score of 99.78%. Additionally, ensemble models combining RF with DTs and GB, as well as ensemble models integrating RF with MLP, demonstrated high accuracy scores of 99.76% and 99.65%, respectively, alongside robust performance across various evaluation criteria. Notably, these proposed models outperformed previous state-of-the-art methods, effectively detecting XSS-based attacks while minimizing FPs and FNs.
Looking ahead, our future work will focus on further enhancing these top-performing models to detect other types of web application attacks, such as SQL injection. By continuing to innovate and refine our approach, we aim to fortify web security measures and stay ahead of emerging cyber threats in an ever-evolving digital landscape. The ML models will be implemented using a large-scale dataset which will improve overall data protection, decrease FPs, and improve real-time monitoring with their smart, adaptable, and effective detection capabilities. They maximize resource use, promote creativity, and may be incorporated into larger security policies. In the end, the ML models are a big step forward in safeguarding sensitive data and web applications from SQL injection attacks and other online threats.
Conceptualization, R.A. and M.A.; methodology, R.A. and M.A.; formal analysis, R.A. and M.A.; investigation, R.A. and M.A.; writing—original draft, R.A.; visualization, R.A.; supervision, M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.
This work utilizes the freely accessible XSS dataset that can be found in [
The authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2024-286).
The authors declare no conflicts of interest.
Footnotes
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![View Image - Figure 7. Results comparison with state-of-the-art methods [22,41,42].](https://media.proquest.com/media/hms/THUM/14/ghnIZ?_a=ChgyMDI2MDMxODA0MjA0OTMzMTo0ODc3MTQSBzEzODMzMjMaCk9ORV9TRUFSQ0giDjIxNi43My4yMTYuMTUyKgcyMDMyMzg0MgozMDg0ODk5OTA2OgtJbmxpbmVJbWFnZUIBMFIGT25saW5lWgNJTEliBFRIVU1qCjIwMjQvMDEvMDFyCjIwMjQvMTIvMzF6AIIBIFAtMTAwMDA4NC0yMDEzOTUtU1RBRkYtbnVsbC1udWxskgEGT25saW5lygEOU2VhcmNoUmV0cmlldmXSARJTY2hvbGFybHkgSm91cm5hbHOaAgdQcmVQYWlkqgIbT1M6RU1TLUZ1bGxUZXh0LWdldEZ1bGxUZXh0ygIPQXJ0aWNsZXxGZWF0dXJl4gIA8gIA%2BgIBWYIDA1dlYooDHENJRDoyMDI2MDMxODA0MjA0OTMzMjo3MTM2NjQ%3D&_s=%2FhEmSvU09yb%2B0orq04zuEZsbgOc%3D "View Image - Figure 7. Results comparison with state-of-the-art methods [22,41,42].")
Summarized dataset’s information.
| Author | No. of Features | Feature Types | No. of Benign Samples | No. of Malicious Samples | Total Number of Samples |
|---|---|---|---|---|---|
| Mokbal et al. [ | 167 | URL, JS, and HTML | 100.000 | 38.569 | 138,569 |
The selected features using IG (25 out of 167) features.
| Feature No. | Feature Name | Feature Description |
|---|---|---|
| 1 | url_length | The length of the URL string in characters. |
| 2 | url_special_characters | The count of special characters (e.g., !, @, #, $) present in the URL. |
| 3 | url_tag_script | Binary indicator (0 or 1) representing whether the URL contains the <script> tag, which is commonly exploited in XSS attacks. |
| 4 | url_cookie | Binary indicator (0 or 1) representing whether the URL contains references to cookies, which may indicate potential security vulnerabilities. |
| 5 | url_number_keywords_param | The count of predefined keywords (e.g., signup, login, query) present as parameters in the URL. |
| 6 | url_number_domain | The count of domains referenced in the URL, which may indicate redirection or external linking. |
| 7 | html_tag_script | Binary indicator (0 or 1) representing whether the HTML content contains the <script> tag, which can execute JS code and potentially lead to XSS vulnerabilities. |
| 8 | html_tag_meta | Binary indicator (0 or 1) representing whether the HTML content contains the <meta> tag, which is used for metadata information and can be manipulated for malicious purposes. |
| 9 | html_tag_link | Binary indicator (0 or 1) representing whether the HTML content contains the <link> tag, which is used to define relationships between documents and can be exploited in XSS attacks. |
| 10 | html_tag_div | Binary indicator (0 or 1) representing whether the HTML content contains the <div> tag, which is commonly used for layout purposes and can be manipulated for XSS attacks. |
| 11 | html_tag_style | Binary indicator (0 or 1) representing whether the HTML content contains the <style> tag, which is used to define styles and can be manipulated to execute malicious code. |
| 12 | html_attr_background | Binary indicator (0 or 1) representing whether the HTML content contains the background attribute, which can be exploited for XSS attacks. |
| 13 | html_attr_href | Binary indicator (0 or 1) representing whether the HTML content contains the href attribute, commonly used for hyperlinks and can be manipulated for XSS attacks. |
| 14 | html_attr_src | Binary indicator (0 or 1) representing whether the HTML content contains the src attribute, commonly used to specify the source of external resources and can be manipulated for XSS attacks. |
| 15 | html_event_onmouseout | Binary indicator (0 or 1) representing whether the HTML content contains the onmouseout event attribute, which can execute JS code when the mouse leaves an element and may be exploited for XSS attacks. |
| 16 | js_file | Binary indicator (0 or 1) representing whether JS files are referenced in the HTML content, which may contain vulnerable code. |
| 17 | js_dom_location | Binary indicator (0 or 1) representing whether the JS code accesses the location object, which can manipulate the URL and may lead to XSS vulnerabilities. |
| 18 | js_dom_document | Binary indicator (0 or 1) representing whether the JS code accesses the document object, which represents the HTML document and can be manipulated for XSS attacks. |
| 19 | js_method_getElementsByTagName | Binary indicator (0 or 1) representing whether the JS code uses the getElementsByTagName() method, which retrieves elements by tag name and may be used in XSS attacks. |
| 20 | js_method_getElementById | Binary indicator (0 or 1) represents whether the JS code uses the getElementById() method, which retrieves an element by its ID and may be exploited for XSS attacks. |
| 21 | js_method_alert | Binary indicator (0 or 1) represents whether the JS code uses the alert() method, which displays an alert dialog box and may be used for XSS attacks. |
| 22 | js_min_length | The minimum length of JS strings in the code. |
| 23 | js_min_function_calls | The minimum number of function calls in the JS code. |
| 24 | js_string_max_length | The maximum length of JS strings in the code. |
| 25 | html_length | The length of the HTML content in characters. |
The experimental results of the proposed models.
| Model | Evaluation Metrics | Confusion Matrix | ||||||
|---|---|---|---|---|---|---|---|---|
| Accuracy | Precision | Recall | F1-Score | TP | FP | TN | FN | |
| LR | 98.28 | 99.38 | 94.32 | 96.79 | 99.78 | 0.22 | 94.32 | 5.68 |
| SVMs | 98.53 | 99.21 | 97.84 | 98.52 | 99.22 | 0.78 | 97.84 | 2.16 |
| MLP | 99.14 | 99.26 | 99.02 | 99.14 | 99.27 | 0.73 | 99.02 | 0.98 |
| ANNs | 99.06 | 99.08 | 99.04 | 99.06 | 99.08 | 0.92 | 99.04 | 0.96 |
| CNNs | 98.82 | 99.57 | 98.07 | 98.81 | 99.57 | 0.43 | 98.07 | 1.93 |
| XGboost | 99.62 | 99.70 | 99.54 | 99.62 | 99.70 | 0.30 | 99.54 | 0.46 |
| DTs | 99.47 | 99.22 | 99.72 | 99.47 | 99.22 | 0.78 | 99.72 | 0.28 |
| RF | 99.78 | 99.80 | 99.75 | 99.78 | 99.80 | 0.20 | 99.75 | 0.25 |
| Ensemble model (MLP with RF) | 99.65 | 99.59 | 99.71 | 99.65 | 99.59 | 0.41 | 99.71 | 0.29 |
| Ensemble model (DTs, RF with GB) | 99.76 | 99.74 | 99.77 | 99.76 | 99.74 | 0.26 | 99.77 | 0.23 |
Results comparison with state-of-the-art methods.
| Author | Methodology | Features | Evaluation Metrics | ||||
|---|---|---|---|---|---|---|---|
| Algorithms | Feature | No. of | Accuracy | Precision | Recall | F1-Score | |
| Mokbal et al. [ | Hybrid (IG and SBS) | 30 | 99.59 | 99.50 | 99.01 | 99.27 | |
| XGBoost | |||||||
| Thajeel et al. [ | DTs | Dynamic | 167 | 98.81 | 98.16 | 97.70 | 97.84 |
| Tariq et al. [ | Genetic algorithm, | - | 167 | 95.38 | 95.93 | 99.54 | 95.20 |
| Our proposed models | RF | IG | 25 | 99.78 | 99.80 | 99.75 | 99.78 |
| DT and RF with GB | 99.76 | 99.74 | 99.77 | 99.76 | |||
| MLP with RF | 99.65 | 99.59 | 99.71 | 99.65 | |||
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