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Sulaiman Mohd Nor
Universiti Teknologi Malaysia
Computer Science & IT

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Metamorphic Malware Detection Based on Support Vector Machine Classification of Malware Sub-Signatures Ban Mohammed Khammas; Alireza Monemi; Ismahani Ismail; Sulaiman Mohd Nor; M.N. Marsono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.3850

Abstract

Achieving accurate and efficient metamorphic malware detection remains a challenge. Metamorphic malware is able to mutate and alter its code structure in each infection, with some vital functionality and codesegment remain unchanged. We exploit these unchanged features for detecting metamorphic malware detection using Support Vector Machine(SVM) classifier. n-gram features are extracted directly from sample malware binaries to avoid disassembly, which are then masked with the extracted Snort signature n-grams. These masked features reduce considerably the number of selected n-gram features. Our method is capable to accurately detect metamorphic malware with ~99 % accuracy and low false positive rate. The proposed method is also superior than commercially available anti-viruses in detecting metamorphicmalware.
Co-Authors Alireza Monemi Ban Mohammed Khammas Ismahani Ismail M.N. Marsono