Analysis of Malware Behaviour: Using Data Mining Clustering Techniques to Support Forensics Investigation

Edem Inang Edem, Chafika Benzaid, Ameer Al-Nemrat, P. Watters
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引用次数: 11

Abstract

The proliferation of malware in recent times have accounted for the increase in computer crimes and prompted for a more aggressive research into improved investigative strategies, to keep up with the menace. Recent techniques and tools that have been developed and adopted to keep up in an arms race with malware authors who have resorted to the use of evasive techniques to avoid analysis during investigation is an on-going concern. Exploring dynamic analysis is unarguably, a positive step to supporting static evidence with malware dynamic behaviour logs. In view of this, analysing this huge generated reports raises concerns about speed, accuracy and performance. This research proposes an Automated Malware Investigative Framework Model, a component based approach that is designed to support investigation by integrating both malware analysis and data mining clustering techniques as part of an effort to solve the problem of overly generated reports. Thus, grouping analysed suspicious samples that exhibit similar behavioural features to make investigation easy and more intuitive. The focus of this paper however, is on implementing sub-components of the framework that directly deals with the problem at hand.
恶意软件行为分析:使用数据挖掘聚类技术支持取证调查
近年来,恶意软件的扩散导致了计算机犯罪的增加,并促使人们对改进的调查策略进行更积极的研究,以跟上威胁的步伐。最近的技术和工具已经被开发和采用,以跟上与恶意软件作者的军备竞赛,恶意软件作者在调查期间使用规避技术来避免分析,这是一个持续的问题。探索动态分析无疑是用恶意软件动态行为日志支持静态证据的积极步骤。鉴于此,分析这些庞大的生成报告引起了对速度、准确性和性能的关注。本研究提出了一种自动化恶意软件调查框架模型,这是一种基于组件的方法,旨在通过集成恶意软件分析和数据挖掘聚类技术来支持调查,作为解决过度生成报告问题的一部分。因此,分组分析了表现出相似行为特征的可疑样本,使调查更容易和更直观。然而,本文的重点是实现直接处理手头问题的框架的子组件。
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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