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Cross-site scripting detection with two-channel feature fusion embedded in self-attention mechanism

2022; Elsevier BV; Volume: 124; Linguagem: Inglês

10.1016/j.cose.2022.102990

1872-6208

Tianle Hu, Chonghai Xu, Shenwen Zhang, Shuangshuang Tao, Luqun Li,

Spam and Phishing Detection

In the era of big data, stealing users’ private data has become one of the main targets of network hackers. In recent years, cross-site scripting (XSS) attacks to obtain users’ privacy data have been one of the main web attack methods of network hackers. Traditional antivirus software cannot identify such cross-site scripting attacks. To identify cross-site scripting attacks quickly and accurately, we proposed a cross-site scripting detection model (C-BLA) with two-channel multi-scale feature fusion embedded in a self-attention mechanism. The model first maps cross-site scripting payloads into spatial vectors by data preprocessing using Word2Vec. Then the two-channel network performs feature extraction on the data. Channel I: extract local features of cross-site scripting payloads at different scales by designing parallel one-dimensional convolutional layers with different convolutional kernel sizes; Channel II: extract semantic information of cross-site scripting payloads from two directions of positive and negative order using a bidirectional Long-Short Term Memory network, and then embed the self-attention mechanism to strengthen the semantic information features. Experiments show that the proposed model achieves a precision rate of 99.8% and a recall rate of 99.1% for cross-site scripting detection, which is a certain improvement in detection rate compared with a single deep learning model and traditional machine learning methods. The two-channel feature fusion of this model better solves the cross-site scripting detection problem.