Ranked linear discriminant analysis features for metamorphic malware detection
Jikku Kuriakose, P. Vinod · 2014
Metamorphic malware modifies the code of every new offspring by using code obfuscation techniques. Recent research have depicted that metamorphic writers make use of benign dead code to thwart signature and Hidden Markov based detectors. Failure in the detection is due to the fact that the malware code appear statistically similar to benign programs. In order to detect complex malware generated with hacker generated tool i.e. NGVCK known to the research community, and the intricate metamorphic worm available as benchmark data we propose, a novel approach using Linear Discriminant Analysis (LDA) to rank and synthesize most prominent opcode bi-gram features for identifying unseen malware and benign samples. Our investigation resulted in 99.7% accuracy which reveals that the current method could be employed to improve the detection rate of existing malware scanner available in public.