Classification of Malware Using Visualisation of Similarity Matrices

S. Venkatraman, M. Alazab
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引用次数: 9

Abstract

Malicious software (malware) attacks are on the rise with the explosion of Internet of Things (IoT) worldwide. With the proliferation of Big Data, it becomes a time consuming process to use various automatic approaches and techniques that are available to detect and capture malware thoroughly. Visualisation techniques can support the malware analysis process for performing the similarity comparisons and summarisation of possible malware in such Big Data contexts. In this paper, we design a novel classification of malware using visualization of similarity matrices. The prime motivation of our proposal is to detect unknown malwares that undergo the innumerable obfuscations of extended x86 IA-32 (opcodes) in order to evade from traditional detection methods. Overall, the high accuracy of classification achieved with our proposed model can be observed visually due to significant dissimilarity of the behaviour patterns exhibited by malware opcodes as compared to benign opcodes.
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基于相似矩阵可视化的恶意软件分类
随着全球物联网(IoT)的爆炸式增长,恶意软件(malware)攻击呈上升趋势。随着大数据的扩散,使用各种可用的自动方法和技术来彻底检测和捕获恶意软件成为一个耗时的过程。可视化技术可以支持恶意软件分析过程,以便在这种大数据环境中对可能的恶意软件进行相似性比较和总结。本文设计了一种基于相似矩阵可视化的恶意软件分类方法。我们提议的主要动机是检测未知的恶意软件,这些恶意软件经历了扩展x86 IA-32(操作码)的无数混淆,以逃避传统的检测方法。总体而言,由于恶意操作码与良性操作码表现出的行为模式存在显著差异,因此可以从视觉上观察到使用我们提出的模型实现的高分类准确性。
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