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Revealing the Feature Influence in HTTP Botnet Detection.

Author(s)
Nur Hidayah M S Department of System and Computer Communication, Faculty of Information and Communications Technology, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
FaizalM A Department of System and Computer Communication, Faculty of Information and Communications Technology, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
Siti Rahayu Selamat Department of System and Computer Communication, Faculty of Information and Communications Technology, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
Rudy Fadhlee M D Department of System and Computer Communication, Faculty of Information and Communications Technology, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
Wan Ahmad Ramzi W Y Department of System and Computer Communication, Faculty of Information and Communications Technology, Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
Abstract
Botnet are identified as one of most emerging threats due to Cybercriminals work diligently to make most of the part of the users' network of computers as their target. In conjunction with that, many researchers has conduct a lot of study regarding on the botnets and ways to detect botnet in network traffic. Most of them only used the feature inside the system without mentioning the feature influence in botnet detection. Selecting a significant feature are important in botnet detection as it can increase the accuracy of detection. Besides, existing research focusses more on the technique of recognition rather than uncovering the purpose behind the selection. Therefore, this paper will reveal the influence feature in botnet detection using statistical method. The result obtained showed the accuracy is about 91% which is approximately acceptable to use the influence feature in detecting botnet activity.
Publication Details
Page(s) 274-281
DOI DOI not available
Published Journal: International Journal of Communication Networks and Information Security, Volume: 9, Issue: 2, Year: 2017

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Keywords
Statistical Approaches, HTTP Botnet, Botnet
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