Blockchain-Fick Gradient Model for Secure MANET Routing and Threat Analytics

Mobile Ad-hoc Networks (MANETs) play a crucial role in defense, disaster relief, and autonomous operations but remain highly exposed to threats such as blackhole, wormhole, and Sybil due to their decentralized topology, while traditional centralized trust mechanisms collapse under dynamic scenarios. This work presents the Blockchain-Fick Gradient Model for Secure MANET Routing and Threat Analytics (FiGRO-CoDpAT), combining blockchain consensus, gradient-based routing, and intelligent intrusion detection. The process begins with Network Initialization using Converged Blockchain Media Consensus (Co-BM-Co) for decentralized node verification. Fick’s Gradient Route Optimizer (FiGRO) then establishes congestion-free, attack-resistant routing. Following this, intrusion detection is performed through the Cosine Dual Phase Aggregator Transformer (CoDpAT), merging Cosine Convolutional Neural Network (CoCNN) and Dual Phase Aggregator Transformer (DpAT) for accurate packet analysis. Blockchain Trust Updates consistently maintain node credibility, while the Mountaineering Team Adaptive Optimizer (MtAO) enhances network efficiency in fluctuating topologies. Simulation findings prove the framework’s effectiveness, reaching an Accuracy of 99.5%, a Packet Delivery Ratio of 99.6%, a Packet Loss of only 0.4%, and a very low delay of 99.72 ms. In summary, FiGRO-CoDpAT provides secure, adaptive, and efficient communication in hostile MANET conditions.