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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding of the Electrical Engineering Computer Science and Informatics
M.N. Marsono
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering

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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.
Cooperative Learning for Distributed In-Network Traffic Classification S.B. Joseph; H.R. Loo; I. Ismail; T. Andromeda; M.N. Marsono
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 3: EECSI 2016
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (861.052 KB) | DOI: 10.11591/eecsi.v3.1144

Abstract

Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative  learning  algorithm  for  propagation and  synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.
Co-Authors Alireza Monemi Andromeda, Trias Ban Mohammed Khammas H.R. Loo I. Ismail Ismahani Ismail S.B. Joseph Sulaiman Mohd Nor