An Improved Ant-IS Algorithm for Intrusion Detection

Abstract

During recent years, the number of attacks on networks has dramatically increased. Consequently the interest in network intrusion detection has increased among the researchers. This paper proposes a clustering Ant-IS and an active Ant colony optimization algorithms for intrusion detection in computer networks. The goal of these algorithms is to extract a set of learning instances from the initial training dataset. The proposed algorithms are an improvement of the previously presented Ant-IS algorithm, used is pattern recognition. Results of experimental tests show that the proposed algorithms are capable of producing a reliable intrusion detection system.