ISSN 1000-1239 CN 11-1777/TP

• 综述 •

### Windows平台恶意软件智能检测综述

1. 1（清华大学网络科学与网络空间研究院 北京 100084）；2（北京信息科学与技术国家研究中心 北京 100084）；3（数学工程与先进计算国家重点实验室 郑州 450002）；4（清华大学软件学院 北京 100084) (wangjl19@mails.tsinghua.edu.cn)
• 出版日期: 2021-05-01
• 基金资助:
国家自然科学基金面上项目(61972224)

### A Survey of Intelligent Malware Detection on Windows Platform

Wang Jialai1,2, Zhang Chao1,2, Qi Xuyan3, Rong Yi4

1. 1（Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing 100084);2(Beijing National Research Center for Information Science and Technology, Beijing 100084);3(State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou 450002);4(School of Software, Tsinghua University, Beijing 100084)
• Online: 2021-05-01
• Supported by:
This work was supported by the General Program of the National Natural Science Foundation of China (61972224).

Abstract: In recent years, malware has brought many negative effects to the development of information technology. In order to solve this problem, how to effectively detect malware has always been a concern. With the rapid development of artificial intelligence, machine learning and deep learning technologies are gradually introduced into the field of malware detection. This type of technology is called intelligent malware detection technology. Compared with traditional detection methods, intelligent detection technology does not need to manually formulate detection rules due to the application of artificial intelligence technology. Besides, intelligent detection technology has stronger generalization capabilities, and can better detect previously unseen malware. Intelligent malware detection has become a research hotspot in the field of detection. This paper mainly introduces current work related to intelligent malware detection, which includes the main parts required for intelligent detection processes. Specifically, we have systematically explained and classified related work for intelligent malware detection in this paper, which includes the features commonly used in intelligent detection, how to perform feature processing, the commonly used classifiers in intelligent detection, and the main problems faced by current malware intelligent detection. Finally, we summarize the full paper and clarify the potential future research directions, aiming to contribute to the development of intelligent malware detection.