使用系统调用轨迹之间的Jensen-Shannon距离识别恶意软件类型

Jeremy D. Seideman, B. Khan, A. C. Vargas
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引用次数: 11

摘要

恶意软件的研究通常涉及某种形式的分组或聚类,以表明密切相关的恶意软件样本。有许多方法可以执行此操作,这取决于为表示恶意软件而记录的数据类型和分组的最终目标。虽然恶意软件家族的概念已经被深入探讨,但我们引入了恶意软件属的概念,恶意软件属是一组由恶意软件种群中样本之间的关系决定的非常密切相关的样本组成的恶意软件。确定恶意软件属的边界取决于恶意软件样本的比较方式和样本之间的整体关系,特别要注意亲子关系。生物学家在建立生物分类学时,有几个标准用来判断一个属的有用性;我们试图设计一种分类方法,使其在恶意软件研究领域和在生物学领域一样有用。我们提出了两个案例研究,其中我们分析了一组恶意软件,使用系统调用轨迹之间的Jensen-Shannon距离来测量样本之间的距离。案例研究表明,我们创建的属符合创建生物有机体分类群时使用的所有标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying malware genera using the Jensen-Shannon distance between system call traces
The study of malware often involves some form of grouping or clustering in order to indicate malware samples that are closely related. There are many ways that this can be performed, depending on the type of data that is recorded to represent the malware and the eventual goal of the grouping. While the concept of a malware family has been explored in depth, we introduce the concept of the malware genus, a grouping of malware that consists of very closely related samples determined by the relationships between samples within the malware population. Determining the boundaries of the malware genus is dependent upon the way that the malware samples are compared and the overall relationship between samples, with special attention paid to the parent-child relationship. Biologists have several criteria that are used to judge the usefulness of a genus when creating a taxonomy of organisms; we sought to design a classification that would be as useful in the world of malware research as it is in biology. We present two case studies in which we analyze a set of malware, using the Jensen-Shannon Distance between system call traces to measure distance between samples. The case studies show the genera that we create adhere to all of the criteria used when creating taxa of biological organisms.
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