Intrusion Detection System Based on Support Vector Machine Optimised with Cat Swarm Optimization Algorithm

Abstract

intrusion detection system (IDS) like firewall, access control and encryption mechanisms no longer provide the muchneeded security for systems and computer networks. Current IDS are developed on anomaly detection which helps to detect known and unknown attacks. Though, these anomaly-based IDS feature a high false rate. To reduce this false alarm rate, in this paper, we proposed an intrusion detection model based on support vector machine (SVM) optimized with Cat swarm optimization (CSO) algorithm. We use the information gain (IG) for attribute reduction and perform classification using the optimized Support vector. The result obtained shows that our model performs well with the least false alarm rate and good accuracy value compare with other classification algorithms evaluated using the same datasets.