Adaptive Tangent Homomorphic Encryption with Equivariant Quantum Neural Networks for Secure Data Transmission Routing in MANET

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.