Visible to the public XSS Attack Detection Methods Based on XLNet and GRU

TitleXSS Attack Detection Methods Based on XLNet and GRU
Publication TypeConference Paper
Year of Publication2021
AuthorsLuo, Jing, Xu, Guoqing
Conference Name2021 4th International Conference on Robotics, Control and Automation Engineering (RCAE)
KeywordsCross Site Scripting, cross-site scripting, Data models, Deep Learning, Dictionaries, Human Behavior, Internet, Network security, pubcrawl, Resiliency, Scalability, Stability analysis, text classification, Training, XLNet, XSS Attacks
AbstractWith the progress of science and technology and the development of Internet technology, Internet technology has penetrated into various industries in today's society. But this explosive growth is also troubling information security. Among them, XSS (cross-site scripting vulnerability) is one of the most influential vulnerabilities in Internet applications in recent years. Traditional network security detection technology is becoming more and more weak in the new network environment, and deep learning methods such as CNN and RNN can only learn the spatial or timing characteristics of data samples in a single way. In this paper, a generalized self-regression pretraining model XLNet and GRU XSS attack detection method is proposed, the self-regression pretrained model XLNet is introduced and combined with GRU to learn the time series and spatial characteristics of the data, and the generalization capability of the model is improved by using dropout. Faced with the increasingly complex and ever-changing XSS payload, this paper refers to the character-level convolution to establish a dictionary to encode the data samples, thus preserving the characteristics of the original data and improving the overall efficiency, and then transforming it into a two-dimensional spatial matrix to meet XLNet's input requirements. The experimental results on the Github data set show that the accuracy of this method is 99.92 percent, the false positive rate is 0.02 percent, the accuracy rate is 11.09 percent higher than that of the DNN method, the false positive rate is 3.95 percent lower, and other evaluation indicators are better than GRU, CNN and other comparative methods, which can improve the detection accuracy and system stability of the whole detection system. This multi-model fusion method can make full use of the advantages of each model to improve the accuracy of system detection, on the other hand, it can also enhance the stability of the system.
Citation Keyluo_xss_2021