Malware Detection Using Hidden Markov Model

Abstract

Malware is a broad term for harmful software that poses significant threats by damaging computer systems and spreading across networks. Traditional detection methods include signature-based and heuristic-based techniques, which are effective against known malware but struggle with new, unknown variants, particularly sophisticated ones like metamorphic, encrypted, and polymorphic viruses. Hence, this research aims at improving malware detection, specifically targeting metamorphic malware that can evade traditional detection methods. The study shows the effectiveness of dynamic analysis over static analysis for detecting metamorphic malware due to its ability to adapt to the malware's constant changes. The dynamic analysis involves examining malware behavior during execution, using dynamic software birthmarks and the Hidden Markov Model to identify malicious activities. It therefore recommends the use of dynamic analysis in the detection of patterns of metamorphic malware. Keywords: Hidden markov models, Malware detection, Dynamic analysis and Static analysis