XSS Attack Detection Based on Multisource Semantic Feature Fusion

Cross-site scripting (XSS) attacks can be implemented through various attack vectors, and the diversity of these vectors significantly increases the overhead required for detection systems. The existing XSS detection methods face issues such as insufficient feature extraction capabilities for XSS attacks, inadequate multisource feature fusion processes, and high resource consumption levels for their detection models. To address these problems, we propose a novel XSS detection approach based on multisource semantic feature fusion. First, we design a normalized tokenization rule based on the structural features of XSS code and use a word embedding model to generate the original feature vectors of XSS. Second, we propose a local semantic feature extraction network based on depthwise separable convolution (DSC) that extracts XSS text and syntactic features using convolution kernels with different sizes. Then, we use a bidirectional long short-term memory (Bi-LSTM) network to extract the global semantic features of XSS. Finally, we introduce a multihead attention fusion network that employs a saliency score and a dynamic weight adjustment mechanism to identify the key parts of the input sequence and dynamically adjust the weight of each head. This enables the deep fusion of local and global XSS semantic features. Experimental results demonstrate that the proposed approach achieves an F1 score of 99.92%, outperforming the existing detection methods.