一种抗混淆的恶意代码变种识别系统

An Anti-obfuscation Malware Variants Identification System

恶意代码变种是当前恶意代码防范的重点和难点.混淆技术是恶意代码产生变种的主要技术,恶意代码通过混淆技术改变代码特征,在短时间内产生大量变种,躲避现有基于代码特征的恶意代码防范方法,对信息系统造成巨大威胁.本文提出一种抗混淆的恶意代码变种识别方法,采用可回溯的动态污点分析方法,配合触发条件处理引擎,对恶意代码及其变种进行细粒度地分析,挖掘其内在行为逻辑,形成可用于识别一类恶意代码的特征,并通过特征融合优化以及权值匹配等方式,提高了对恶意代码变种的识别能力.通过实验,验证了本文的识别方法对恶意代码及其混淆变种的识别能力.

Malware variants are one of the major challenges in malware detecting today.Obfuscation,as a most popular technology to generate these variants,can change the signatures of malware to avoid the current signature-based malware preventing method,which is a big threat to information system.This paper proposes a novel anti-obfuscate malware detecting method.By making use of dynamic taint analysis methods and trigger-based behavior processing engine,this method can abstract the essential behavior logic of malware in fine-grained and form it as signatures of a class of malware,and identify variants more precisely associated with signature merging optimizing process and fuzzy matching methods.Experiment results show that the detecting method in this paper can identify malwares and its variants efficiently.