An efficient algorithm for anomaly intrusion detection in a network

Abstract

As the number of intrusions is increasing, intrusion detection of systems and network infrastructures Systems (IDS) is now an active research area to develop reliable and efficient detection and countering solutions. Finding the efficient methods for intrusion detection in information and network security is a crucial step and that in this study proposed an evolutionary approach for intrusion detection that is more efficient and effective. Evolutionary algorithms have been demonstrated in the IDS over the times, its maturity. Although most research is carried out on genetic algorithms which have their merits and demerits. In this paper, we present an optimized algorithm viz. Genetic-based Enhanced grey wolf optimization (GB-EGWO) Algorithm for intrusion detection. The number of feature selections for the proposed algorithm was selected from the new FS algorithm to increase IDS performance. In this study, the benchmark NSL-KDD network intrusion was applied to evaluate the proposed algorithm modified from the 99-data KDD cup to evaluate IDS issues. Simulation results prove its effectiveness over the existing work and have achieved better accuracy.