关键词: XSS攻击检测, 深度学习, 卷积神经网络（CNN）, ResNet, 门控循环单元（GRU）

Abstract: Traditional XSS attacks and their vulnerability detection methods are difficult to achieve satisfactory results in the face of diverse attack payloads, which require a lot of manual involvement and are highly subjective, and deep learning methods such as CNN and RNN can only learn spatial features or temporal features of data samples in a single way. This paper proposes an XSS attack detection method based on residual network and GRU, which introduces a residual framework based on CNN and combines GRU to learn the spatio-temporal features of data, and improves the generalization ability of the model by using dropout. In the face of the increasingly complex and variable XSS payload, this paper refers to character-level convolution to build a dictionary to encode the data samples so as to preserve the features of the original data and improve the overall efficiency, and then transforms it into a two-dimensional spatial matrix to make it meet the input requirements of CNN. The experimental results on the Github dataset show that the accuracy of this paper is 99.92% and the false alarm rate is 0.02%, the accuracy is 11.09 percentage points higher and the false alarm rate is 3.95 percentage points lower than the DNN method, and the other evaluation indexes are better than those of the GRU and CNN comparison methods.

Key words: XSS attack detection, deep learning, convolutional neural network（CNN）, ResNet, gate recurrent unit（GRU）