Trust-aware Secure Routing and Intrusion Detection in MANETs Using Dilated Convolutional Multi-Relational Graph Attention Network

Mobile Ad hoc Networks (MANETs) are a rapidly developing technology, making their security a major concern. In order to enable trust-aware and attack-resilient routing, the goal of this research is to develop an intrusion detection system (S-IDS) based on deep learning (DL). By leveraging trust values of the nodes using Osprey Optimization Algorithm (OOA), the presented Dilated Convolutional Multi-Relational Graph Attention Network (DConMRG-Net) based Intrusion Detection System (IDS) identifies potential intruders, ensuring that the paths generated within the MANET are reliable and resilient. Additionally, a novel optimization algorithm namely, Hybrid Adaptive Genghis Khan Shark Gold Rush Optimization (HAGKS-GRO) Algorithm is introduced by combining the Adaptive Genghis Khan Shark Optimization (AGKSO) Algorithm with Gold Rush Optimization (GRO) Algorithm for optimal path selection. Two situations are examined: one in which there is no attack and the other in which there is an attack. Relevant performance metrics are evaluated in connection with these scenarios, including throughput, packet delivery ratio, attack identification rate, accuracy, error rate and computation time. Evaluation results demonstrate significant improvements, with a maximum detection rate of 99% with a minimum computational time of 55ms for with attack case and 51ms for without attack case. The proposed model surpasses the state-of-the-art attack detection techniques and achieves high efficiency, according to the simulation results.