Intrusion Detection Using Whale Optimization Based Weighted Extreme Learning Machine in Applied Nonlinear Analysis

Abstract

The development of computer networks and technology has led to a sharp rise in network assaults, making network intrusion a crucial area for research and the development of counter measures. The issue of network intrusion can be resolved with the advancement of artificial intelligence. We employ a Whale Optimization Algorithm (WOA) based Weighted Extreme Learning Machine (WELM) in this study to identify network intrusions. Prior to training the neural network and obtaining the output weight, ELM randomly allocates weight to the network. The ELM weights need to be optimized, thus we're utilising the whale optimisation technique to do this. To compare and evaluate the effectiveness of the model suggested in this study, NSL-KDD dataset is utilised. The experimental findings demonstrate that weighted ELM-based whale optimisation outperformed other approaches in detecting network intrusions and reducing false positive and false negative rates.