Enhanced intrusion detection system based on bat algorithm-support vector machine

As new security intrusions arise so does the demand for viable intrusion detection systems These solutions must deal with huge data volumes, high speed network traffics and countervail new and various types of security threats. In this paper we combine existing technologies to construct an Anomaly based Intrusion Detection System. Our approach improves the Support Vector Machine classifier by exploiting the advantages of a new swarm intelligence algorithm inspired by the environment of microbats (Bat Algorithm). The main contribution of our paper is the novel feature selection model based on Binary Bat Algorithm with Levy flights. To test our model we use the NSL-KDD data set and empirically prove that Levy flights can upgrade the exploration of standard Binary Bat Algorithm. Furthermore, our approach succeeds to enhance the default SVM classifier and we obtain good performance measures in terms of accuracy (90.06%), attack detection rate (95.05%) and false alarm rate (4.4%) for unknown attacks.