Survey on Deep-learning-based Malware Identification Research

The proliferation of malware poses a serious security threat to individual users, enterprises, industrial production, and networks. The complexity of malware is rapidly changing with the development of artificial intelligence technologies, and the struggle between security analysts and malware developers is essentially a game between people. The identification methods based on signature and heuristic rules are not enough to cope with the development of new malware. Compared with traditional detection methods, intelligent detection techniques based on deep learning technologies are better able to detect previously unseen malware because they do not require manual development of detection rules and have stronger generalization capabilities. The application of machine learning and deep learning techniques in malware detection has become a hot topic of current research. This survey aims to provide a basic overview of deep learning-based malware detection techniques for Windows platforms, to help researchers understand the field of malware detection, and to explore new developments and research directions.