基于行为的勒索软件检测

C. Chew, Vimal Kumar
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引用次数: 10

摘要

勒索软件在网络安全领域是一个日益增长的威胁,目标是易受攻击的用户和公司,但目前缺乏的是一种更简单的方法来分组,并设计出日常用户可以使用的实用而简单的解决方案。在本文中,我们研究了勒索软件的不同特征,并提出了解决这些勒索软件攻击的预防技术。更具体地说,我们的技术是基于勒索软件的行为,而不是大多数反恶意软件使用的基于签名的检测。我们进一步讨论了这些技术的实施及其有效性。我们在WannaCry、TeslaCrypt、Cerber和Petya这四种著名的勒索软件上测试了这些技术。在本文中,我们讨论了我们的技术如何处理这些勒索软件菌株以及这些技术对性能的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Behaviour Based Ransomware Detection
Ransomware is an ever-increasing threat in the world of cyber security targeting vulnerable users and companies, but what is lacking is an easier way to group, and devise practical and easy solutions which every day users can utilise. In this paper we look at the different characteristics of ransomware, and present preventative techniques to tackle these ransomware attacks. More specifically our techniques are based on ransomware behaviour as opposed to the signature based detection used by most anti-malware software. We further discuss the implementation of these techniques and their effectiveness. We have tested the techniques on four prominent ransomware strains, WannaCry, TeslaCrypt, Cerber and Petya. In this paper we discuss how our techniques dealt with these ransomware strains and the performance impact of these techniques.
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