Design of an Intelligent Model for Wireless Data Security Using Graph Neural Cryptonets and Evolutionary Protocol Synthesis

Abstract

The need for robust data security mechanisms is growing with increasing reliance on wireless networks for mission-critical communication. While most approaches for wireless data security concentrate on a static cryptographic implementation or protocol-level defenses that show static responses to dynamic threat environments, they have become increasingly vulnerable against evolving attack vectors like spoofing, worm holes, and denial of service when talking about the networking scenario. As such, traditional validation frameworks lack statistical rigor and cannot quantify the resilience of protocols under varying network conditions. To fill in the gaps, this work introduces a comprehensive multi-layered security architecture incorporating five novel analytical models that directly address the prime concerns of wireless data security. The Graph Neural Cryptonet (GNC-Net) is performatively integrated cryptography-related functions within the protocol to realize encrypted, trust-aware routing tables. An Adaptive Statistical Defense Engine (ASDE) integrates Bayesian data drift detection for real-time network traffic anomaly identification. The Quantum-Encoded Multipath Protocol Selector (QEMPS) provides simulated quantum key distribution for securing dynamic multipath routing sets. The Cross-Entropy Protocol Evaluation Model (CEPEM) provides an opportunity to analyze, in statistical terms, comparing protocol resilience against threat through distribution divergence analysis. Finally, the Hybrid Algorithmic Synthesizer using Evolutionary Security Heuristics (HASEH) generates barrier-free automatic secure routing algorithms through evolutionary programming, combining optimizations with performance and resilience. The collaborative web of these methods empowers a series of actions oriented toward proactive threat mitigation, adaptive protocol synthesis, and statistically validated security optimization. Experiments show a gain in packet integrity (+38%), decreased attack surface (–45%), and increased anomalous detection latency of <1.6s. The suggested framework sets the stage for secure-and-adaptive development in statistically solid wireless networks, pushing the state of the art in wireless data protection sets.