Abstract:
The rapid expansion of Internet of Things devices presents significant security challenges that traditional perimeter-based security models fail to handle. Existing IoT security solutions, which are mostly perimeter based, often lack the dynamic, context-aware measures needed to address evolving threats in distributed environments. This research introduces a novel trust score model integrated with Zero Trust Architecture principles and Anomaly Detection for IoT networks. The main objective of the study is to monitor the behavior of the IoT devices and assess their security status in real-time. The approach incorporates continuous authentication, least privilege access, and micro-segmentation within a practical IoT testbed. Security analysis is enhanced through a trust scoring system, behavioral monitoring, and a Zero Trust Policy Engine. To assess device trust scores, a trust score equation is developed using the Analytical Hierarchy Process (AHP), which is employed due to its effectiveness in handling multiple security attributes. AHP's structured decision-making framework ensures accurate trust score calculation in a real time environment. This score dynamically adjusts based on selected attributes, enabling contextual and responsive threat mitigation. The proposed model’s strength is significantly improved by tightly integrating with ML-based anomaly detection offering improved responsiveness to emerging threats. By embedding these principles into a scalable ZTA framework, this research offers guidelines for implementation and contributes to IoT security with a comprehensive, adaptable defense mechanism. The simulation results from various vulnerable and benign scenarios reveal distinct trust score variations, highlighting the model's effectiveness in evaluating the vulnerability level of IoT devices. The study demonstrates that a trust score-based ZTA can address the limitations of static security models, providing a practical solution for the expanding IoT landscape.
