Mobile malware visual analytics and similarities of Attack Toolkits (Malware gene analysis)

Anand Paturi, Manoj Cherukuri, John Donahue, Srinivas Mukkamala
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引用次数: 21

Abstract

We use Normalized Compression Distance (NCD) (owing to its capabilities to perform similarity measure of unstructured data) to enumerate code similarity between malicious Android apps and visualize their clusters. Our classification methods and visual analytics can help the antivirus community to ensure that a variant of a known malware can still be detected without the need of creating a signature. We also present when a new malware is released, our methods can be used to understand the similarity/behavior with known malware families.
移动恶意软件可视化分析及攻击工具包相似性(恶意软件基因分析)
我们使用规范化压缩距离(NCD)(由于它能够执行非结构化数据的相似性度量)来枚举恶意Android应用程序之间的代码相似性并可视化它们的集群。我们的分类方法和可视化分析可以帮助反病毒社区确保在不需要创建签名的情况下仍然可以检测到已知恶意软件的变体。当一个新的恶意软件发布时,我们的方法可以用来理解与已知恶意软件家族的相似性/行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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