Integrated Deep Learning and Cryptographic Optimization for Secure, Adaptive and Quantum Resilient Wireless Networks

The increasing complexity and scale of the modern wireless networks introduced challenges for ensuring real-time security, adaptive routing and congestion control especially under energy constraints and quantum-era threats in process.Usually, existing solutions handle these dimensions in isolation, based on static rule-based models incapable of adapting to dynamic conditions; for instance, changing traffic patterns, evolving attacks and heterogeneous node capabilities.Such limitations necessitate the definition of an integrated framework that optimizes QoS and security concurrently while being adaptive, decentralized and forward-compatible to requirements of post-quantum security.Thus, this work proposes an integrated suite of five tightly coupled models: (i) a deep learning-based traffic-aware reinforcement adaptive protocol (DL TRAP) with CNN embedded into MAPS AOMDV for congestion prediction and adaptive multipath routing, (ii) federated edge architecture (FED EARN) utilizing homomorphic encryption offloading with energy-aware RSA computation; (iii) CRYPT GRAN, a behavior-derived-uniform key-length encryption using Trust Informed Encryption Depth Control; (iv) Q-learning-based reinforcement traffic rerouting (REIN ACT) for attack classification and (v) quantum resistant elastic routing and trust framework (Q EDRT) based on NTRUEncrypt and Dempster-Shafer theory for post-quantum secured communications.All these methods together create a strong prediction and adaptive secure communication structure.Numerical results widen friction of improvement, introducing a stint of reduced latency (of almost 48%), encryption delay (up to 50%), excellent attack classification accuracy (96.2%), and trust propagation reliability (>95%).In the long run, this work contributes to building a resilient wireless network by weaving intelligent routing, energy-aware cryptography, and quantumsafe trust propagation into one unified real-time adaptive system in process.