Multi-objective functions in particle swarm optimization for intrusion detection

The paper constitutes the study of particle swarm optimization in multi-objective functions. Swarm intelligence plays a vital role in intrusion detection. Intrusion detection system identifies the normal as well as abnormal behavior of a system. The weighted aggregation method is considered as multi-objective functions. We propose an intrusion detection mechanism based on particle swarm optimization which has a strong global search capability and used for the dimensionality optimization. Random forest is used as the classifier for modelling attacks and legitimate set. An accuracy of 91.71% at detection time of 0.22sec is obtained.