Malware Detection Using Machine Learning Based on the Combination of Dynamic and Static Features

被引：0

作者：

Zhao, Jingling ^{[1
]}

Zhang, Suoxing ^{[1
]}

Liu, Bohan ^{[1
]}

Cui, Baojiang ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing, Peoples R China

来源：

2018 27TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND NETWORKS (ICCCN) | 2018年

基金：

美国国家科学基金会;

关键词：

malware detection; dynamic feature; static feature; binary program instrumentation; machine learning;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

As millions of new malware samples emerge every day, traditional malware detection techniques are no longer adequate. Static analysis methods, such as file signature, fail to detect unknown programs. Dynamic analysis methods have low efficiency and high false positive rate. We need a detection technique that can adapt to the rapidly changing malware ecosystem. The paper presented a new malware detection method using machine learning based on the combination of dynamic and static features. The characteristic of this experiment involved in many fields of knowledge, including binary program instrumentation, static analysis, assembly instruction analysis, machine learning, etc. Finally, we achieved a good result over a substantial number of malwares.

引用

页数：6