A Distributed Hybrid Extended Kalman Filtering–Machine Learning Model for Trust-Based Authentication and Authorization in IoT Networks

The physical layer security of Internet of Things (IoT) networks has become increasingly important but also introduces major security vulnerabilities due to the open and shared nature of wireless channels. Therefore, authentication and authorization remain critical challenges. To address these issues, this paper proposes a lightweight hybrid authentication framework that integrates Extended Kalman Filter (EKF)-based signal refinement with machine learning (ML) classification to strengthen device trust verification at the physical layer. The framework operates across device, edge, and cloud tiers, utilizing real-time received signal strength indicator (RSSI), link quality indicator (LQI), temperature, and battery level to generate unique device fingerprints. The EKF minimizes environmental noise and extracts stable signal characteristics, while the XGBoost classifier provides adaptive and efficient authentication. Experimental results show that the proposed hybrid model achieves 99.56% accuracy, a 99.71% F1-score, and a very low false acceptance rate. These findings confirm that the EKF–ML integration enhances signal stability and resistance to spoofing, offering a secure and scalable authentication solution for IoT networks.