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Cross-site scripting attack detection based on a modified convolution neural network
Yan, Huyong1,2,3,4; Feng, Li5; Yu, You6; Liao, Weiling5; Feng, Lei7,8; Zhang, Jingyue4; Liu, Dan9; Zou, Ying9; Liu, Chongwen1,2,3; Qu, Linfa10
2022-08-29
摘要Cross-site scripting (XSS) attacks are currently one of the most threatening network attack methods. Effectively detecting and intercepting XSS attacks is an important research topic in the network security field. This manuscript proposes a convolutional neural network based on a modified ResNet block and NiN model (MRBN-CNN) to address this problem. The main innovations of this model are to preprocess the URL according to the syntax and semantic characteristics of XSS attack script encoding, improve the ResNet residual module, extract features from three different angles, and replace the full connection layer in combination with the 1*1 convolution characteristics. Compared with the traditional machine learning and deep learning detection models, it is found that this model has better performance and convergence time. In addition, the proposed method has a detection rate compared to a baseline of approximately 75% of up to 99.23% accuracy, 99.94 precision, and a 98.53% recall value.
关键词XSS URL ResNet word vector code injection
DOI10.3389/fncom.2022.981739
发表期刊FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
卷号16页码:13
通讯作者Feng, Lei(fenglei@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000883130600001
语种英语