Swarm Intelligence-Based Intrusion Detection Framework Using Neural Network & Bee Colony Optimiation

Abstract

An Intrusion Detection System (IDS) serves as a critical defense mechanism for safeguarding networks against unauthorized activities and cyber attacks. However, the processing of sophisticated datasets with contemporary detection methodologies often presents challenges due to their intricate scale, complicating the identification of complex threats. This study aims to enhance IDS operational efficacy through the development of a novel method integrating Bee Colony Optimization (BCO) and Neural Networks (NN). Employing a quasi-experimental design, the research evaluates the system's performance, demonstrating that the integration of BCO significantly optimizes neural network functionality, thereby improving both the speed of attack detection and the accuracy of feature selection. Utilizing the NSL-KDD dataset, the proposed framework notably minimizes false alerts while augmenting overall detection accuracy levels. The findings underscore that advancements in cybersecurity systems can be achieved through the synergy of Neural Networks and Swarm Intelligence technology, providing effective solutions for real-time intrusion detection systems. This research not only contributes to the theoretical understanding of IDS optimization but also has practical implications for enhancing cybersecurity measures in various organizational contexts.