文章基本信息

标题：A Survey on Malware Detection and Analysis Tools
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作者：Sajedul Talukder ; Zahidur Talukder
期刊名称：International Journal of Network Security & Its Applications
印刷版ISSN：0975-2307
电子版ISSN：0974-9330
出版年度：2020
卷号：12
期号：2
页码：37-57
DOI：10.5121/ijnsa.2020.12203
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The huge amounts of data and information that need to be analyzed for possible malicious intent are one of the big and significant challenges that the Web faces today. Malicious software, also referred to as malware developed by attackers, is polymorphic and metamorphic in nature which can modify the code as it spreads. In addition, the diversity and volume of their variants severely undermine the effectiveness of traditional defenses that typically use signature-based techniques and are unable to detect malicious executables previously unknown. Malware family variants share typical patterns of behavior that indicate their origin and purpose. The behavioral trends observed either statically or dynamically can be manipulated by using machine learning techniques to identify and classify unknown malware into their established families. This survey paper gives an overview of the malware detection and analysis techniques and tools.
关键词：Malware;Detection;Analysis;Tools;Machine Learning.