Intelligent malware detection based on graph convolutional network

Malware has seriously threatened the safety of computer systems for a long time. Due to the rapid development of anti-detection technology, traditional detection methods based on static analysis and dynamic analysis have limited effects. With its better predictive performance, AI-based malware detection has been increasingly used to deal with malware in recent years. However, due to the diversity of malware, it is difficult to extract feature from malware, which make malware detection not conductive to the application of AI technology. To solve the problem, a malware classifier based on graph convolutional network is designed to adapt to the difference of malware characteristics. The specific method is to firstly extract the API call sequence from the malware code and generate a directed cycle graph, then use the Markov chain and principal component analysis method to extract the feature map of the graph, and design a classifier based on graph convolutional network, and finally analyze and compare the performance of the method. The results show that the method has better performance in most detection, and the highest accuracy is 98.32%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$98.32\%$$\end{document}, compared with existing methods, our model is superior to other methods in terms of FPR and accuracy. It is also stable to deal with the development and growth of malware.