Optimizing Deep Neural Network using Enhanced Artificial Bee Colony Algorithm for an Efficient Intrusion Detection System

Abstract

Owing to ongoing rapid developments in network related technologies combined with the great surge in their usage, the methodologies for cyber-attacks like intrusions are also constantly modernizing leading to a greater rate of accuracy, effect and frequency of such network-related issues. In this research exercise, we establish an innovative and efficient methodology for Deep Learning-based solutions for Intrusion detection. To establish this, we propose a Deep Neural Network (DNN) trained by an Enhanced Artificial Bee Colony Algorithm for efficient and accurate intrusion detection over wireless and interconnected environments. This research effort constitutes a holistic and comparative analysis of the complete functionality and technicality of the proposed system. The proposed model performed much better than many other state-of-the-art models. Furthermore, the comprehensive explanation provided by this research can be leveraged into the development of more precocious and modern Intrusion Detection System.