Improving the detection of metamorphic malware through data dependency graphs indexing

Abstract

Metamorphism have been successfully used in original malicious code to the creation and proliferation of new malware instances, making them harder to detect. This work presents an approach that identifies metamorphic malware through data dependency graphs comparison. Features are extracted on data dependency graphs to build an index that is used to determine which malware family a suspicious code belongs to. Experimental results on 3045 samples of metamorphic malware showed that our proposed approach obtained accuracy rate higher than most commercial anti-malware tools.