Malicious Node Identification in Mobile Adhoc Networks Leveraging Genetic Algorithm and Adaptive Neuro-Fuzzy Inference System

Security threat is the chief problem in ‘mobile adhoc networks’ (MANET). The effectiveness of the MANET is influenced via the malicious nodes’ existence. It is an extremely hard job for recognizing the malicious nodes (MN) as of the faithful nodes in MANET owing to related traits betwixt MA and trusty node. An effective feature extraction centered MN detection system with GA in addition to ANFIS approach is proposed. Features like trust factor, service trust features, and weightage are extorted as of trusty and MNs. The finest attributes are selected utilizing GA; the extorted sorts are proficient in addition categorized utilizing ANFIS. The proposed MN detection’s performance on MANET is examined concerning ordinary packet loss ratio, throughput, and also detection ratio. The outcomes showed the domination of the proposed work.