Back
 JIS  Vol.7 No.3 , April 2016
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.
Cite this paper: Alqurashi, S. and Batarfi, O. (2016) A Comparison of Malware Detection Techniques Based on Hidden Markov Model. Journal of Information Security, 7, 215-223. doi: 10.4236/jis.2016.73017.
References

[1]   Annachhatre, C., Austin, T.H. and Stamp, M. (2015) Hidden Markov Models for Malware Classification. Journal in Computer Virology and Hacking Techniques, 11, 59-73.
http://dx.doi.org/10.1007/s11416-014-0215-x

[2]   Bazrafshan, Z., Hashemi, H., Fard, S.M.H. and Hamzeh, A. (2013) A Survey on Heuristic Malware Detection Techniques. The 5th Conference on Information and Knowledge Technology (IKT 2013), Shiraz, 28-30 May 2013, 113-120.
http://dx.doi.org/10.1109/ikt.2013.6620049

[3]   Wong, W. (2006) Analysis and Detection of Metamorphic Computer Viruses. MSc, San Jose State University.

[4]   Wong, W. and Stamp, M. (2006) Hunting for Metamorphic Engines. Journal in Computer Virology, 2, 211-229.
http://dx.doi.org/10.1007/s11416-006-0028-7

[5]   Bayer, U., Moser, A., Kruegel, C. and Kirda, E. (2006) Dynamic Analysis of Malicious Code. Journal in Computer Virology, 2, 67-77.
http://dx.doi.org/10.1007/s11416-006-0012-2

[6]   Venkatesan, A. (2008) Code Obfuscation and Virus Detection. MSc, San Jose State University.

[7]   Dastidar, S.G., Mandal, S. and Barbhuiya, F.A. (2012) Detecting Metamorphic Virus Using Hidden Markov Model and Genetic Algorithm. Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011). India, 20-22 December 2011.

[8]   Priyadarshi, S. (2011) Metamorphic Detection via Emulation Metamorphic Detection via Emulation. San Jose State University.

[9]   Austin, T.H., Filiol, E., Josse, S. and Stamp, M. (2013) Exploring Hidden Markov Models for Virus Analysis: A Semantic Approach. 46th Hawaii International Conference on System Sciences, Wailea, 7-10 January 2013, 5039-5048.
http://dx.doi.org/10.1109/hicss.2013.217

[10]   Rabiner, L.R. (1989) A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proc. IEEE, 77, 257-286.
http://dx.doi.org/10.1109/5.18626

[11]   Annachhatre, C. (2013) Hidden Markov Models for Malware Classification. San Jose State University.

[12]   Krogh, A. (1998) An Introduction to Hidden Markov Models for Biological Sequences. Computational Methods in Molecular Biology, 32, 45-63.
http://dx.doi.org/10.1016/s0167-7306(08)60461-5

[13]   Stamp, M. (2004) A Revealing Introduction to Hidden Markov Models. Dep. Comput. Sci. San Jose State, 1-20.

[14]   Kazi, S. (2012) Hidden Markov Models for Software Piracy Detection. San Jose State University,

[15]   Desai, P. (2008) Towards an Undetectable Computer Virus. Intelligence, 1, 402-427.

[16]   Kalbhor, A., Austin, T.H., Filiol, E., Josse, S. and Stamp, M. (2014) Dueling Hidden Markov Models for Virus Analysis. Journal in Computer Virology and Hacking Techniques, 11, 103-118.

[17]   Thunga, S.P. and Neelisetti, R.K. (2016) Identifying Metamorphic Virus Using N-Grams and Hidden Markov Model. 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, 2015, 2016-2022.

[18]   Payandeh, A. (2014) Detecting Encrypted Metamorphic Viruses by Hidden Markov Models. 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Xiamen, 2014, 973-977.

 
 
Top