A Framework for Dynamic Malware Analysis Based on Behavior Artifacts

被引:6
作者
Paul, T. G. Gregory [1 ]
Kumar, T. Gireesh [1 ]
机构
[1] Amrita Univ, Amrita Sch Engn, Amrita Vishwa Vidyapeetham, TIFAC CORE Cyber Secur, Coimbatore, Tamil Nadu, India
来源
PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON FRONTIERS IN INTELLIGENT COMPUTING: THEORY AND APPLICATIONS, FICTA 2016, VOL 1 | 2017年 / 515卷
关键词
Dynamic malware analysis; Cuckoo sandbox; Features extraction; Machine learning;
D O I
10.1007/978-981-10-3153-3_55
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Malware stands for malicious software. Any file that causes damage to the computer or network can be termed as malicious. For malware analysis, there are two fundamental approaches: static analysis and dynamic analysis. The static analysis focuses on analyzing the file without executing, whereas dynamic analysis means analyzing or observing its behavior while it is being executed. While performing malware analysis, we have to classify malware samples. The different types of malware include worm, virus, rootkit, trojan horse, back door, botnet, ransomware, spyware, adware, and logic bombs. In this paper, our objective is to have a breakdown of techniques used for malware analysis and a comparative study of various malware detection/classification systems.
引用
收藏
页码:551 / 559
页数:9
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