SWARM BASED CLASSIFIER MODEL USING ENSEMBLE FEATURE RANKING METHODS

• Published in: ARPN journal of engineering and applied sciences 2015-10, Vol.10 (18), p.8052-8059
• Main Authors: Amudha, P, Karthik, S, Sivakumari, S
• Format: Article
• Language: English
• Subjects: Ant colony optimization; Classifiers; Computer information security; Intrusion; Ranking; Security; Support vector machines; Traffic flow
• ISSN: 1819-6608; 1819-6608

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. 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 SSO and hybridization of SSO with Support Vector Machine (SVM). It is shown that the hybridization of SSO with Ant Colony Optimization using hybrid feature ranking method outperformed other algorithms and can be efficient in the detection of intrusive behaviour.