NDLI: Using hidden markov model for dynamic malware analysis: First impressions

Content Provider	IEEE Xplore Digital Library
Author	Imran, M. Afzal, M.T. Qadir, M.A.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Mohammad Ali Jinnah Univ., Islamabad, Pakistan (Imran, M.; Afzal, M.T.; Qadir, M.A.)
Abstract	Malware developers are coming up with new techniques to escape malware detection. Furthermore, with the common availability of malware construction kits and metamorphic virus generators, creation of obfuscated malware has become a child's play. This has made the task of anti-malware industry a challenging one, who need to analyze tens of thousands of new malware samples everyday in order to provide defense against the malware threat. The silver lining is that most of the malware generated by such means is different only syntactically, and hence techniques employing dynamic analysis and behavior modeling can be effectively used for classifying malware. In this paper we have proposed a malware classification scheme based on Hidden Markov Models using system calls as observed symbols. Our approach combines the powerful statistical pattern analysis capability of Hidden Markov Models with the proven capacity of system calls as discriminating dynamic features for countering malware obfuscation. Testing the proposed technique on system call logs of real malware shows that it has the potential of effectively classifying unknown malware into known classes.
Starting Page	816
Ending Page	821
File Size	193235
Page Count	6
File Format	PDF
e-ISBN	9781467376822
DOI	10.1109/FSKD.2015.7382048
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Computer science Analytical models Hidden Markov models dynamic malware analysis Machine learning Feature extraction Malware Hidden Markov Model malware classification
Content Type	Text
Resource Type	Article

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