A hybrid network intrusion detection system using simplified swarm optimization (SSO)

Chung, Yuk Ying and Wahid, Noorhaniza (2012) A hybrid network intrusion detection system using simplified swarm optimization (SSO). Applied Soft Computing, 12 . ISSN 15684946

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Official URL: http://dx.doi.org/10.1016/j.asoc.2012.04.020

Abstract

The network intrusion detection techniques are important to prevent our systems and networks from malicious behaviors. However, traditional network intrusion prevention such as firewalls, user authentication and data encryption have failed to completely protect networks and systems from the increasing and sophisticated attacks and malwares. In this paper, we propose a new hybrid intrusion detection system by using intelligent dynamic swarm based rough set (IDS-RS) for feature selection and simplified swarm optimization for intrusion data classification. IDS-RS is proposed to select the most relevant features that can represent the pattern of the network traffic. In order to improve the performance of SSO classifier, a new weighted local search (WLS) strategy incorporated in SSO is proposed. The purpose of this new local search strategy is to discover the better solution from the neighborhood of the current solution produced by SSO. The performance of the proposed hybrid system on KDDCup 99 dataset has been evaluated by comparing it with the standard particle swarm optimization (PSO) and two other most popular benchmark classifiers. The testing results showed that the proposed hybrid system can achieve higher classification accuracy than others with 93.3% and it can be one of the competitive classifier for the intrusion detection system.

Item Type:Article
Uncontrolled Keywords:particle swarm optimization; local search; classification; data mining; network intrusion detection
Subjects:T Technology > T58.5-58.64 Information technology
Divisions:Faculty of Science Computer and Information Technology > Department of Information System
ID Code:3212
Deposited By:En. Muhamad Saufi Che Rusuli
Deposited On:07 Nov 2012 15:49
Last Modified:07 Nov 2012 15:49

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