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.

Due to the dynamic nature of the network architecture, resource constraints, and susceptibility to security attacks, securing data transmission in Mobile Ad-hoc Networks (MANETs) is a significant problem. This work proposes a novel Equivariant Quantum Neural Networks with Adaptive Tangent Brakerski-Gentry Vaikuntanathan Homomorphic Encryption algorithm (EQNN-ATBGVHEA)- based secure routing in MANET. The suggested approach comprises three steps: cluster head (CH) selection, optimal path selection, and secure data transfer. Initially, the Bowerbird Optimization Algorithm chooses the CH and sends the message through the constructed path. Once the clusters are established, data is transferred between the sender and receiver. For optimal route selection, developed the EQNNs technique which incorporates a neural network for quick route selection. EQNN resolves the issues of local optimality by constructing a new fitness process based on residual energy (RE) and delay. After the optimal path selection, Data transfer is secured by the innovative ATBGVHEA technique. Furthermore, this method is built using NS3, and the variables are determined. Additionally, the acquired results are contrasted with existing approaches for validating the efficiency of the suggested strategy. The developed method achieved a clustering accuracy of 98.5%, a computational time of 55ms, and a residual energy of 0.44.