A framework for metamorphic malware analysis and real-time detection

摘要

Metamorphism is a technique that mutates the binary code using different obfuscations. It is difficult to write a new metamorphic malware and in general malware writers reuse old malware. To hide detection the malware writers change the obfuscations (syntax) more than the behavior (semantic) of such a new malware. On this assumption and motivation, this paper presents a new framework named MARD for Metamorphic Malware Analysis and Real-Time Detection. As part of the new framework, to build a behavioral signature and detect metamorphic malware in real-time, we propose two novel techniques, named ACFG (Annotated Control Flow Graph) and SWOD-CFWeight (Sliding Window of Difference and Control Flow Weight). Unlike other techniques, ACFG provides a faster matching of CFGs, without compromising detection accuracy; it can handle malware with smaller CFGs, and contains more information and hence provides more accuracy than a CFG. SWOD-CFWeight mitigates and addresses key issues in current techniques, related to the change of the frequencies of opcodes, such as the use of different compilers, compiler optimizations, operating systems and obfuscations. The size of SWOD can change, which gives anti-malware tool developers the ability to select appropriate parameter values to further optimize malware detection. CFWeight captures the control flow semantics of a program to an extent that helps detect metamorphic malware in real-time. Experimental evaluation of the two proposed techniques, using an existing dataset, achieved detection rates in the range 94%-99.6%. Compared to ACFG, SWOD-CFWeight significantly improves the detection time, and is suitable to be used where the time for malware detection is more important as in real-time (practical) anti-malware applications.