SECURING NETWORKS: ADVANCEMENT IN INTRUSION DETECTION, AODV, CLUSTER-BASED ROUTING, USING MACHINE LEARNING TECHNIQUES.

Abstract

In today's interconnected world, network security remains a paramount concern, necessitating constant advancements in intrusion detection and routing protocols. This journal explores the forefront of network security with a focus on the integration of machine learning techniques such as KNN (K-Nearest Neighbors), AMODV (Adaptive Multiobjective Optimization-based Detection of Anomalies in Network Traffic), and IDS (Intrusion Detection System) into traditional approaches. Beginning with an examination of state-of-the-art intrusion detection models like KNN-IDS, the journal delves into the intricacies of ad hoc multipath distance vector (AODMV) routing, cluster-based routing protocols, and their incorporation with intrusion detection systems. Through detailed analyses and case studies, it elucidates the challenges and opportunities inherent in securing networks against evolving threats. Furthermore, the journal investigates the synergistic potential of machine learning techniques in enhancing the efficacy and adaptability of intrusion detection and routing systems. By synthesising theoretical insights with practical implementations of KNN, AMODV, IDS, and routing protocols like CBRP (Cluster-Based Routing Protocol), this work provides a comprehensive overview of the latest advancements in network security, offering valuable insights for researchers, practitioners, and policymakers alike.