Quantum Resilient Explainable Autonomous IoT Security Framework Using Dual‐Layer Blockchain for Edge Intelligence

Abstract

The fast explosion of Internet of Things (IoT) technology across smart homes, autonomous vehicles, healthcare, and industrial infrastructure has led to real‐time data transmission, which is highly susceptible to sophisticated cyber‐attacks. The scalability of security protocols, trust management, and robustness against quantum‐based attacks is limited for existing security protocols. To address these challenges, this research proposes a Quantum Resilient Explainable framework with Dual Layer Blockchain (QRE‐DLB) for secure edge intelligence. The proposed framework utilizes CRYSTALS‐Kyber512 and Dilithium2 for quantum‐resistant authentication and communication, whereas dual‐layer blockchain technology is used for decentralized authorization and trust verification, which is achieved through static cryptographic identities and dynamic behavioral analysis. For intelligent threat detection, the trio‐based rethinking transformer‐enhanced temporal convolutional network is used for adaptive anomaly detection, whereas the multi‐view heterogeneous deterministic‐probabilistic graph attention network is used for attack detection. The proposed framework achieves 99.4% detection accuracy, 99.3% precision, 99.2% recall, 24% reduction in detection latency, 18% reduction in energy consumption, and 13.7% improvement in attack mitigation rate. These findings demonstrate the practical applicability of QRE‐DLB for secure edge‐enabled IoT deployments and provide an effective engineering solution for building scalable, explainable, and quantum‐resilient cyber‐physical systems.