Using a Grey Wolf Optimization and Multilayer Perceptron Algorithms for an Anomaly-Based Intrusion Detection System

Abstract

The swift development of information technology has led to an increase in the total number of electronic devices linked to the Internet. Additionally, there were more network attacks. Accordingly, it is crucial to create a defense system capable of identifying novel attack types. An intelligent system Intrusion detection system (IDS) is the most effective defense system, monitoring and analyzing network packets to spot any unusual activity. Moreover, there are a lot of useless and repetitive features in the network packets, that hurt the IDS system's performance and use up too many resources. The computation times will be shortened and computation complexity will be also simplified by choosing the suitable feature selection technique that helps to determine the most related subset of features. An enhanced anomaly IDS model based on a multi-objective grey wolf optimization technique has been proposed in this paper. Using the grey wolf optimization technique, the best features from the dataset were identified to achieve a considerable improvement in classification accuracy. However, a multilayer perceptron technique (MLP) was employed to assess the suitability of specific features that were properly for predicting attacks. Furthermore, to show the efficiency of the suggested approach using 20% of the NSL-KDD dataset, multiple attack scenarios were employed. The proposed approach achieves high detection rates (92.52%, 70.31%, 14.53%, and 2.87%) for DoS, Probe, R2L, and U2R categories, respectively, with classification accuracy reaching 85.43%. Our proposed model was evaluated against other current approaches and produced noteworthy results.