Universiti Teknologi Malaysia Institutional Repository

Finding granular features using rough-PSO in IDS

Zainal, Anazida and Maarof, Mohd. Aizaini and Shamsuddin, Siti Mariyam (2007) Finding granular features using rough-PSO in IDS. In: Fifth International Conference on Information Technology in Asia 2007, 9-12th July 2007, Kuching, Sarawak, Malaysia.

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Most of the existing IDS use all the features in network traffic to evaluate and look for known intrusive pallerns. Unfortunately, such system suffers a lengthy detection procedure. Serious implication may incur to a host computer or network due to delay in diagnosis. Feature reduction improves the speed of data manipulation and classification rate by reducing the influence of noise. Besides, selecting important features from input data leads to a simplification of a problem, faster and more accurate detection rates. The purpose of this paper is to investigate the effectiveness of the Rough Set and Particle Swarm (PSG) in feature selection. Support Vector Machine (SVM) was used as a classifier. Data used in this experiment was originally obtained from dataset created by DARPA in the framework ofthe 1998 Intrusion Detection Evaluation Program. Six significantfeatures were proposed by Rough-PSG.

Item Type:Conference or Workshop Item (Paper)
Additional Information:ISBN: 983-9257-66-8
Uncontrolled Keywords:intrusive pallerns, Support Vector Machine (SVM)
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Computer Science and Information System
ID Code:10107
Deposited By: Narimah Nawil
Deposited On:26 Aug 2010 08:58
Last Modified:29 Feb 2020 13:43

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