Detection and Mitigation of Black Hole Attack Using Fuzzy Heuristics in Mobile AdHoc Network (MANET)

Abstract

Detection of attacks in Ad Hoc Networks for Mobile(MANETs) employs anomaly and intrusion activity detection, intrusion detection systems (IDS), trust-based approaches, collaborative detection, and game-theoretic methods. These techniques monitor network behavior, identify malicious nodes, detect known attack signatures, assess node trustworthiness, promote cooperative detection, and model node interactions. By integrating diverse detection mechanisms, MANETs can effectively identify and mitigate security threats, ensuring reliable communication in dynamic environments. This study aims to develop a system for detecting and preventing black hole attacks in wireless ad hoc networks using fuzzy heuristics. The approach involves defining fuzzy logic rules and heuristics to identify abnormal activity indicative of black hole attacks in network traffic. Algorithms are designed to analyse network parameters such as packet routing, signal strength, and node behaviour using fuzzy logic techniques. Attack detection mechanisms are implemented to dynamically adjust network parameters based on fuzzy inference to mitigate the impact of black hole attacks. Anomaly detection and prevention modules are integrated into existing network protocols to enhance security against black hole attacks. The effectiveness of the fuzzy heuristic-based approach is evaluated through simulation and real-world experimentation, considering factors such as detection accuracy, false positive rate, and network performance.