A Simulation-Based Quantum-Synchronized Ephemeral Encryption Framework for QKD-Secured IoT Networks with Transformer-Based Cyber-Quantum Attack Detection

This paper presents a simulation-based cyber-quantum Internet of Things (IoT) security framework for modeling, securing, and detecting attacks in QKD-secured IoT communication environments. The proposed framework integrates heterogeneous IoT traffic generation, gateway-assisted routing, edge processing, QKD key-pool management, Quantum-Synchronized Ephemeral Encryption (Q-SEE), cross-layer adversarial attack injection, and AI-based multiclass detection. Unlike conventional IoT intrusion datasets that mainly capture packet- or flow-level abnormalities, the generated dataset represents the joint behavior of IoT sessions, network delay, queue pressure, QKD state, key consumption, encryption-mode transitions, ciphertext metadata, and cyber-quantum risk. A Python/SimPy/NetworkX simulation was developed using 80 IoT devices, 3 gateways, 2 edge servers, 4 cyber-quantum control-plane nodes, and 1 adversarial orchestrator. The final simulation produced 46,351 records with 76 features covering normal traffic, five traditional IoT attacks, and six novel cyber-quantum attacks, including QKD key-pool starvation, QBER camouflage, false QKD-health injection, encryption downgrade induction, queue–key coupling, and multi-vector cyber-quantum orchestration. Q-SEE adaptively selects among QKD-OTP, QKD-synchronized AES-256 ephemeral mode, PQC fallback, degraded mode, and blocked mode according to QBER, secret key rate, key availability, device criticality, downgrade pressure, and risk. A leakage-aware Quantum-Aware Kolmogorov–Arnold Network (QKAN) was then trained using deployable cyber-quantum evidence. The final nonrisk QKAN achieved 98.79% test accuracy, 98.61% macro-F1, 98.85% weighted-F1, and 99.78% macro-AUC, demonstrating effective detection of traditional and cyber-quantum IoT attacks.