Approach of Zero Trust Security to Improve Internet of Things Infrastructure Security (LogicLink, 2025)
The heterogeneity and resource constraints of Internet of Things (IoT) devices render traditional perimeter security inadequate. This study proposes a Zero Trust Security (ZTS) framework for IoT infrastructures that integrates a novel dynamic policy engine with continuous authentication and AI-assisted anomaly detection. The framework was evaluated in a simulated IoT environment using the TON_IoT dataset. Experimental results demonstrate that the proposed model achieved a 92.5% detection accuracy, reduced average response latency to 1.76 seconds, and decreased unauthorized access attempts by 87.1%. The key novelty lies in the architecture's context-aware feedback loop, where anomaly findings directly and adaptively inform access policies in real-time, a mechanism not extensively explored in prior ZTS models for IoT. These findings confirm that integrating ZTS with intelligent analytics significantly enhances IoT security resilience. This framework offers a practical blueprint for implementing robust, context-aware security in large-scale IoT applications, such as smart cities and industrial automation.
Keywords: Zero Trust Security; Internet of Things; Infrastructure Security; Artificial Intelligence; Anomaly Detection