Research On Cross-site Attack Detection Based On Graph Convolutional Neural Network

The continuous development of Internet technology has provided convenience for people’s daily life and work.The browser has become a tool for most people to access the Internet.At the same time,network security issues have also become a topic of concern.Cross-site scripting attack is a common network attack method,which uses vulnerabilities in websites to carry out attacks and steal users’ personal information.Traditional filtering methods have difficulties to detect complex malicious scripts.Many researchers use machine learning algorithms and deep learning models which rely on the feature description of the data to detect malicious scripts.However,the attack is a dynamic process.The defense technology is strengthened day by day,and the attack technology is also gradually developing.Therefore,the description of data characteristics has certain limitations.The graph convolutional neural network is developed from the traditional convolutional neural network.The characteristics of the data are updated by learning the connections between the data,which can effectively solve the problem caused by traditional models only focusing on the characteristics of the data itself.Therefore,this thesis proposes a Cross-site Scripting attack detection method based on Graph Convolutional Neural Network.The main research work is as follows:(1)Apply graph convolutional neural network to cross-site scripting attack detection.Compared with traditional deep learning models,graph convolutional neural networks can learn the similarities between data and are more suitable for the detection of cross-site scripting attacks.(2)Data acquisition and pre-processing.In order to ensure the validity and versatility of the model,the XSS attack data was obtained through the XSSED website,and the normal data was obtained through Git Hub to form the XSSED data set.Due to the inconsistency of the obtained data format,this article preprocessed all the obtained data,and used the regular matching method to segment the data.(3)Build graph structure.When composing the graph,this article no longer focuses on the feature description of the data itself,but focuses on the similarity between the data and converts the data into a weighted graph.This paper uses PMI and TF-IDF to calculate the weight value between data.(4)Introduce the softmax mechanism to the pooling layer.This paper introduces the softmax mechanism in the pooling layer to realize the merging of data nodes through edge selection.This method not only ensures that the data will not be deleted,but also achieves the purpose of dimension’s reduction.