Mobile Ad Hoc Networks (MANETs) are decentralized in nature and, therefore, they have no centralized control, and consequently, they are highly susceptible to routing attacks like black hole attacks and gray hole attacks, both of which disable data delivery by causing a vicious loss of packets. To address these issues, the current study offers the Quantum-resilient Machine Learning and Q-Learning-driven Priority Time-Slot AODV (QR-MLQ-PTS-AODV) routing model. This framework combines a multi-metric trust query, an entropy-based behavioral stability query, a temporal query trust adjustment, and a managed machine learning method to attain exact malicious node forecasting. Reinforcement learning, through Q -learning, is employed to utilize dynamical assignment of MAC -layer priority time slots to enable cross-layer optimization, as well as adaptive routing decisions. In contrast to solutions that exist, the suggested framework avoids quantum-vulnerable cryptographic primitives in favor of hash-based trust authentication and learning-based mitigation measures, to make sure that it can withstand novel quantum-assisted routing attacks. The limited variables of the trust model are determined by a mathematical analysis and extensive NS-3 simulations that show that the model significantly improves the ratio of packet delivery, end-to-end delay, routing overhead, and attack detection accuracy in comparison with traditional AODV and the most up-to-date trust-, ML-, and RL-based protocols. Based on these results, the effectiveness of embedding quantum-sensitive security protocols and smart cross-layer routing in MANETs can be supported.
Reddy et al. (Thu,) studied this question.