Enhanced Protection for IoT System With Intelligent Swift Scan Quantum‐Resilient Intrusion Detection System ( ISS ‐ QR ‐ IDS )

ABSTRACT The Internet of Things (IoT) is particularly vulnerable in this new era, as IoT devices often rely on lightweight encryption and security measures due to their limited processing capabilities and power constraints. Existing signature‐based intrusion detection strategies are inadequate against the advanced and adaptive nature of quantum attacks, which can exploit the polymorphic and metamorphic behavior of quantum‐enhanced malware. This research addresses the challenges posed by a possible attack scenario named the “Quantum‐Enhanced Cloak Malware (QECM) Attack” and aims to provide enhanced protection to IoT systems against this sophisticated threat. The proposed “Intelligent Swift Scan Quantum‐Resilient Intrusion Detection System (ISS‐QR‐IDS)” integrates advanced techniques to enhance detection speed and accuracy, mitigate risks associated with polymorphic and metamorphic malware behaviors, and secure communication channels against quantum threats. The model incorporates the Parallel Vario‐Isolation Detector, which combines Variational Autoencoders (VAEs) and Parallelized Isolation Forests to detect quantum‐enhanced malware, and Hypergraph Attention Networks (HGA‐Net), leveraging Hypergraph Neural Networks (HGNNs) and Graph Attention Networks (GATs) to detect the critical interactions and improve anomaly detection accuracy. Additionally, postquantum cryptographic algorithms like NTRU Encrypt and FALCON ensure secure communication channels and data integrity. By combining these advanced techniques, ISS‐QR‐IDS aims to provide a robust defense mechanism against sophisticated cyber threats targeting IoT networks, ensuring their security and resilience in the face of quantum computing advancements.