Title: Intelligent intrusion detection system using multilayer perceptron optimised by genetic algorithm
Authors: Mehdi Moukhafi; Khalid El Yassini; Seddik Bri
Addresses: Informatics and Applications Laboratory (IA), Faculty of Sciences, Department of Mathematics and Computer Science, Moulay Ismail University, Meknes, Morocco ' Informatics and Applications Laboratory (IA), Faculty of Sciences, Department of Mathematics and Computer Science, Moulay Ismail University, Meknes, Morocco ' Materials and Instrumentations (MIN), Department of Electrical Engineering Superior School of Technology: ESTM, Moulay Ismail University, Meknes, Morocco
Abstract: This paper presents a neural network-based intrusion detection method for the attacks on a computer network. Neural networks are used to predict unusual activities in the system. In particular, feed forward neural networks with the back propagation training algorithm were employed in this study. We propose a method of intrusion detection based on a combination of genetic algorithm (GA) and multilayer perceptron (MLP) neural network to develop a model for intrusion detection system. All tests were realised with the kdd99 data set. The performance of the proposed method of intrusion detection was evaluated on all KDD99 data set, 10% of the KDD99 data set were used for training the GA-MLP model. This system achieves a top accuracy of up to 93.05%.
Keywords: machine learning-based intrusion detection; parameters optimisation; genetic algorithm; multilayer perceptron neural network.
International Journal of Computational Intelligence Studies, 2020 Vol.9 No.3, pp.190 - 199
Received: 08 May 2018
Accepted: 07 Mar 2019
Published online: 04 Sep 2020 *