Effective gorilla troops optimization-based hierarchical clustering with HOP field neural network for intrusion detection

Abstract

An intrusion detection system (IDS) armed with signature and attack pattern databases as reference tools are used to protect computer networks from intrusion. This article provides a hybrid machine learning algorithm for gorilla troops optimization (GTO) integrating hierarchical clustering and Hopfield neural network. In this paper, the authors present a model to improve intrusion detection accuracy and contain high operational flexibility of these techniques. It is inspired by social behavior in gorillas and optimizes the clustering process HNN. Experimental results show that the proposed approach enhances the traditional methods in intrusion detection for a variety of intrusions and it presents an effective solution that can help cybersecurity application development better.