Universiti Teknologi Malaysia Institutional Repository

obfuscated computer virus detection using machine learning algorithm

Tan, H. X. and Ismail, I. and Khammas, B. M. (2019) obfuscated computer virus detection using machine learning algorithm. Bulletin of Electrical Engineering and Informatics, 8 (4). pp. 1383-1391. ISSN 2089-3191


Official URL: http://www.dx.doi.org/10.11591/eei.v8i4.1584


Nowadays, computer virus attacks are getting very advanced. New obfuscated computer virus created by computer virus writers will generate a new shape of computer virus automatically for every single iteration and download. This constantly evolving computer virus has caused significant threat to information security of computer users, organizations and even government. However, signature based detection technique which is used by the conventional anti-computer virus software in the market fails to identify it as signatures are unavailable. This research proposed an alternative approach to the traditional signature based detection method and investigated the use of machine learning technique for obfuscated computer virus detection. In this work, text strings are used and have been extracted from virus program codes as the features to generate a suitable classifier model that can correctly classify obfuscated virus files. Text string feature is used as it is informative and potentially only use small amount of memory space. Results show that unknown files can be correctly classified with 99.5% accuracy using SMO classifier model. Thus, it is believed that current computer virus defense can be strengthening through machine learning approach.

Item Type:Article
Uncontrolled Keywords:machine learning, obfuscated computer virus, signature based
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:Electrical Engineering
ID Code:91933
Deposited By: Narimah Nawil
Deposited On:09 Aug 2021 16:46
Last Modified:09 Aug 2021 16:46

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