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Malware classification and composition analysis: A survey of recent developments
Journal of Information Security and Applications ( IF 5.6 ) Pub Date : 2021-04-26 , DOI: 10.1016/j.jisa.2021.102828
Adel Abusitta , Miles Q. Li , Benjamin C.M. Fung

Malware detection and classification are becoming more and more challenging, given the complexity of malware design and the recent advancement of communication and computing infrastructure. The existing malware classification approaches enable reverse engineers to better understand their patterns and categorizations, and to cope with their evolution. Moreover, new compositions analysis methods have been proposed to analyze malware samples with the goal of gaining deeper insight on their functionalities and behaviors. This, in turn, helps reverse engineers discern the intent of a malware sample and understand the attackers’ objectives. This survey classifies and compares the main findings in malware classification and composition analyses. We also discuss malware evasion techniques and feature extraction methods. Besides, we characterize each reviewed paper on the basis of both algorithms and features used, and highlight its strengths and limitations. We furthermore present issues, challenges, and future research directions related to malware analysis.



中文翻译:

恶意软件分类和组成分析:最新动态调查

鉴于恶意软件设计的复杂性以及通信和计算基础架构的最新发展,恶意软件的检测和分类正变得越来越具有挑战性。现有的恶意软件分类方法使反向工程师可以更好地了解其模式和分类,并应对其发展。此外,已经提出了新的成分分析方法来分析恶意软件样本,目的是对它们的功能和行为有更深入的了解。反过来,这有助于反向工程师识别恶意软件样本的意图并了解攻击者的目标。该调查对恶意软件分类和组成分析中的主要发现进行分类和比较。我们还将讨论恶意软件规避技术和特征提取方法。除了,我们根据所使用的算法和功能对每篇论文进行特征分析,并强调其优势和局限性。我们还将介绍与恶意软件分析相关的问题,挑战和未来的研究方向。

更新日期:2021-04-27
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