Novel Multi-Objective Artificial Bee Colony Optimization for Wrapper Based Feature Selection in Intrusion Detection

This study proposes a novel approach based on multi-objective artificial bee colony (ABC) for feature selection, particularly for intrusion-detection systems. The approach is divided into two stages: generating the feature subsets of the Pareto front of non-dominated solutions in the first stage and using the hybrid ABC and particle swarm optimization (PSO) with a feed-forward neural network (FFNN) as a classifier to evaluate feature subsets in the second stage. Thus, the proposed approach consists of two stages: (1) using a new feature selection technique called multi-objective ABC feature selection to reduce the number of features of network traffic data and (2) using a new classification technique called hybrid ABC–PSO optimized FFNN to classify the output data from the previous stage, determine an intruder packet, and detect known and unknown intruders.