Behavioral Profiling–Driven Zero-Trust Security Framework for Smart City IoT Ecosystems
Uman Ahmed Mohammed
Abstract
Over the past few years, the intensive growth of city IoT systems has triggered the prevalence of heterogeneous devices, thus creating complex and highly dynamic ecosystems that significantly increase the size of the potential attack surface. Traditional perimeter-based security concepts and passive, identity-based controls are insufficient to ensure such environments are protected against advanced adversarial approaches, hacked devices and abnormal post-authentication activities. Based on which, we offer a Behavioral Profiling-Driven Zero-Trust Architecture (BP-ZTA) that integrates machine-learning-assisted behavioral analytics with zero-trust concepts to provide a continuously adaptive and context-aware security to smart-city IoT networks. The framework uses multi-layer behavioral modelling at the edge, fog, and cloud layer, using anomaly detection, graph-based learning, and reinforcement learning to generate dynamic scores of device-trust. A policy engine then consumes these trust scores and coordinates real-time micro-segmentation of access-control decisions. It is designed to support both hybrid-cloud and multi-hyperscaler deployments, with the containerised microservices, infrastructure components prepared with AI, and federated learning pipelines. This architecture guarantees scale ability, privacy and availability. Empirical assessments show that the BP-ZTA framework significantly increases the accuracy of anomaly-detection and the false-positiveness rate as well as makes smart-city infrastructures more resilient without compromising latency and resource-effective use. The study provides an end-to-end security model consistent with the current enterprise architecture and customizable to a broad spectrum of applications of smart cities.