Performance optimization of multipath K-AOMDV protocol using SVM against blackhole attack
Abstract
This research focuses on the Ad Hoc On-Demand Multi-Path Routing (AOMDV) protocol, which is preferred for its improved efficiency compared to a single-path routing protocol in mobile ad hoc networks (MANETs). However, identifying attackers in such networks is a complicated task due to malicious nodes providing optimistic, forward-looking optimistic responses. In this study, the author proposes a novel security solution, the K-AOMDV (KNN- Ad Hoc On-Demand Multi-Path Routing protocol) that uses K-means clustering to prevent routing misbehaviour. The efficiency of the proposed K-AOMDV routing protocol is analyzed using supervised machine learning approach to predict optimal routes with minimal packet hops between nodes. The proposed algorithm has a high accuracy rate of 0.99%, 80% true positives, and 80% recall. It communicates the black hole attacker’s node identification (ID) into the network, ensuring that the attacker will not participate in the routing method in the future. The privacy domain in MANET is the main focus of this research, and the proposed solution affiliates an effective approach to enhancing the security of MANETs.
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DOI: https://doi.org/10.32629/jai.v7i5.1488
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