A Comparison of Malware Detection Techniques Based on Hidden Markov Model

Abstract

Malware is a software which is designed with an intent to damage a network or computer resources. Today, the emergence of malware is on boom letting the researchers develop novel techniques to protect computers and networks. The three major techniques used for malware detection are heuristic, signature-based, and behavior based. Among these, the most prevalent is the heuristic based malware detection. Hidden Markov Model is the most efficient technique for malware detection. In this paper, we present the Hidden Markov Model as a cutting edge malware detection tool and a comprehensive review of different studies that employ HMM as a detection tool.