A Survey of Intelligent Malware Detection on Windows Platform

被引：0
|
作者：
Wang J. [1 ,2 ]
Zhang C. [1 ,2 ]
Qi X. [3 ]
Rong Y. [4 ]
机构：
[1] Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing
[2] Beijing National Research Center for Information Science and Technology, Beijing
[3] State Key Laboratory of Mathematical Engineering and Advanced Computing, Zhengzhou
[4] School of Software, Tsinghua University, Beijing
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2021年 / 58卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Artificial intelligence; Deep learning; Intelligent malware detection; Machine learning; Malware;
D O I：
10.7544/issn1000-1239.2021.20200964
中图分类号：
学科分类号：
摘要：
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. © 2021, Science Press. All right reserved.
引用
收藏
页码：977 / 994
页数：17
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