| Abstract | The widespread integration of Internet of Things (IoT) devices has enhanced the intelligence of homes, industries, and offices, yet it introduces critical security challenges due to their susceptibility to dynamic threats and behavioral heterogeneity, necessitating identification via communication patterns rather than mere physical recognition. This paper addresses the demand for a unified security framework in IoT ecosystems, where devices, limited by diverse protocols and constrained computational resources, face attacks such as DNS tunneling, MAC spoofing, and several other threats. Existing approaches, which rely on coarse-grained signatures or segregated machine learning for device identification and intrusion detection, exhibit limited resilience, increased operational overhead, poor cross-network adaptability, and scalability constraints in real-time dynamic settings. We propose iPASecIoT, a single-model framework that concurrently identifies IoT devices and detects intrusions using fine-grained behavioral fingerprints. Our methodology combines machine and deep learning algorithms with a modified firefly algorithm employing a kappa score-based voting mechanism for adaptive feature selection, yielding a lightweight, resource-efficient model by optimizing agreement beyond chance across network traffic, inter-arrival times, and protocol-specific features. Evaluated on the CICIoMT2024, CICIoT2023, and UNSW2019 datasets, iPASecIoT achieves mean F1 scores of 99.99 %, 99.88 %, and 98.35 % for device identification and 99.96 %, 99.38 %, and 98.79 % for threat classification across the CICIoMT2024, CICIoT2023, and UNSW2019 datasets, respectively. With a mean inference time of 0.0005 seconds per sample and a mean Hamming loss of ≈ 0.001, iPASecIoT provides a pioneering, efficient, and scalable solution to counter evolving security threats in heterogeneous IoT environment. |
|---|
