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Intrusion detection is one of the most significant concerns in network safety. Improvements have been proposed from various perspectives. One such proposal is improving the classification of packets in a network to determine whether the classified packets contain harmful packages. In this study, we present a new approach for intrusion detection using a hybrid of the artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) to develop an artificial neural network with increased precision in classifying malicious from harmless traffic in a network. Our proposed algorithm is compared with four other algorithms employed in WEKA tool, namely, radial basis function, voted perceptron, logistic regression, and multilayer perceptron. The system is first prepared, and then suitable biases and weights, for the feed-forward neural network are selected using the hybrid ABC-PSO algorithm. Then, the network is retrained using the prepared information, which has been generated from the ideal weights and biases, to establish the intrusion-detection model. The KDD Cup 1999 data set is used as the information set in comparing our proposed algorithm with the other algorithms. The experiment shows that the proposed method outperforms the four classification algorithms and is suitable for network intrusion detection. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]