Quantum-Driven Anomaly Detection Framework for Consumer IoT Cyber-Physical Systems

This study aims to enhance the security of Consumer IoT (CIoT) systems by addressing the limitations of traditional anomaly detection approaches. To achieve this, we propose the Quantum-Driven Adaptive Anomaly Detection Framework (QADAPT), a novel model designed to enable real-time anomaly detection through a quantum-inspired adaptive cognitive mapping function. The framework is built upon a multilayered architecture consisting of a Quantum-State Convolutional Layer, Synthetic Verification Layer, and Adaptive Mapping Layer, allowing simultaneous data state analysis and validation against synthetic signals. Q-ADAPT uses an adaptive deep learning model to recognize evolving CIoT behavior patterns, enhancing detection accuracy and resilience under varying noise conditions. The simulation environment spans a time frame of 340 minutes, designed to evaluate the robustness of the model in six distinct scenarios under Gaussian noise. Performance results reveal that Q-ADAPT achieves a detection accuracy of 97.8% in lowcomplexity environments and maintains 91.3% under high-noise conditions.