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CNNPayl: An Intrusion Detection System of Cross-site Script Detection

In order to effectively improve the accuracy of cross-site scripting attack detection, a method based on CNNPayl for XSS attack detection is proposed. Firstly, the attack vector is generated and transformed by using the HMM model and code obfuscation strategy to obtain the test attack data set. Using sample preprocessing, all sample data sets are processed by normalization and word segmentation to reduce redundant information. Secondly, Word2vec is used to convert the preprocessing results into word embedding, and the relationship between HTML tags and DOM methods is obtained, and the network structure is reduced. The complexity is further studied by constructing four convolutional layers to learn and extract semantic features, and to achieve effective classification through the SOFTMAX layer. The experimental results show that the proposed method reduces the false positive rate and improves the detection accuracy.