Mitigating Sinkhole Attacks in MANET Routing Protocols using Federated Learning HDBNCNN Algorithm

In network topology, the sinkhole attacks ensue while a malicious node deceives it’s adjust mode in the routing traffic and it results a predominant interruptions like amplified latency, depletion of energy with conceded data reliability. Moreover a decentralized feature of MANETs are specifically exposed to treats and emphasize the necessity of distinct solutions. In this study, Federated Learning is utilized to improve the security and privacy through empowering nodes to train the model deprived of distribution complex data. Then, every node gathers information about the local routing and contributes towards an inclusive model, which captures behaviour of the entire network when conserving its specific privacy. Further, the Hierarchical Deep Belief Network Convolutional Neural Network (HDBNCNN) algorithm has analysed the accumulated data in detecting the anomalies revealing the sinkhole activity centred on learning routing patterns. Besides, detection, the model can implement mitigation approaches, which includes redirecting traffic from conceded nodes and informing the routing tables to reinforce flexibility from imminent attacks. The proposed study aims to enhance the reliability and efficiency of MANETs in attack situations when minimizing overhead from malicious traffic management. By combining progressive ML methods through decentralized network structure, hence the study expressively provides a secure communication in MANETs and contributes a scalable and effective results through evaluating its performance metrics.