Hybridized Swarm Optimization Classifiers with Ensemble Feature Ranking Techniques: Recent Study | Chapter 8 | Emerging Trends in Engineering Research and Technology Vol. 6

Intrusion Detection System (IDS) is a security support mechanism which has become an essential component of security infrastructure to detect attacks, identify and track the intruders. Intrusion Detection Systems are implemented in order to detect malicious activities and it functions behind the firewall, observing for patterns in network traffic that might indicate malicious action. The extreme development of the internet, the high occurrence of the threats over the internet has been the cause in recognizing the need for both IDS and firewall to help in securing a network. Currently many researchers have shown an increasing interest in intrusion detection based on data mining techniques and swarm intelligence techniques. Also, recent research focuses more on the hybridization of techniques to improve the performance of classifiers and it has become commonplace in IDSs which allows researchers to exploit the benefits of individual techniques and approaches. In intrusion detection, the quantity of data is huge that includes thousands of traffic records with number of various features. Selecting a subset of informative features can lead to improved classification accuracy. In this paper ensemble of feature ranking techniques are used to select the most relevant features that can represent the pattern of the network traffic. The efficiency of the presented method is validated on KDDCUP’99 dataset using hybrid swarm based classifier, Simplified Swarm Optimization (SSO) with Ant Colony Optimization (ACO). The performance of the proposed method is compared with the basic classifiers, SSO and hybridization of SSO with Support Vector Machine (SVM). It is shown that the hybridization of SSO with ACO using hybrid feature ranking method outperformed other algorithms and can be efficient in the detection of intrusive behaviour.

Author(s) Details

P. Amudha
Department of CSE, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India

S. Sivakumari
Department of CSE, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India.

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