Hybridized Swarm Optimization Classifiers with Ensemble Feature Ranking Techniques: Recent Study

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.