A Vulnerability Detection Method for Internet Cross-Site Scripting Based on Relationship Diagram Convolutional Networks

Abstract

The aim of this research is to quickly detect cross-site scripting (XSS) attacks on the internet based on relationship diagram convolutional networks. Based on the principle and attack process of cross-site scripting attacks, domain knowledge is used to build an XSS ontology to conduct high-level modeling of cross-site scripting attacks, obtain data that can reflect XSS attacks, normalize these attack data, extract attack data word vectors, use them as the input of the relationship diagram convolution networks added to the attention mechanism, and learn attack feature word vectors. After further extracting node characteristics through convolution and pooling, all node characteristics are aggregated and fed into the fully connected neural network. XSS vulnerability detection results are obtained through classification of the activation function, and malicious domain name and malicious IP information are combined as supplementary rules to improve the effectiveness of the vulnerability detection in internet cross-site scripting based on the relationship graph convolution network. Experiments show that this method can accurately detect XSS vulnerabilities, provide comprehensive and accurate attack details, and its performance is better than that of the literature method, which is reflected in the higher accuracy, recall, accuracy and F1 value, and the leading area of the ROC curve. Its detection speed is extremely fast, only 0.03 s, and by combining malicious domain name and IP information, the detection efficiency is further improved, realizing rapid response and effectively maintaining Internet security.