Multi-objective particle Swarm optimization in intrusion detection

Abstract

In this paper, we proposed particle swarm optimization using multiobjective functions. Intrusion detection system has a significant role in research methodology. Intrusion detection system identifies the normal as well as abnormal behavior of a system. Swarm intelligence plays an essential role in intrusion detection. Random forest classifier is used for detecting attacks. Intrusion detection mechanism based on particle swarm optimization which has a strong global search capability is used for dimensionality optimization. Weighted aggregation method is employed as multi-objective functions. The proposed system has the high intrusion detection accuracy of 97.54 % with a detection time is 0.20 s.