An AI-Based Solution for Secure Service Provisioning in IoT

2026-06-29

AI Summary

This study addresses the expanding attack surface and security-reliability challenges in IoT service provisioning. To tackle these issues, the authors propose an intelligent service selection framework that integrates deep reinforcement learning with federated learning. For the first time, a behavior fingerprint–driven distributed reliability scoring mechanism is embedded into the service decision-making pipeline, enabling adaptive device selection that simultaneously satisfies functional requirements and security constraints in dynamic environments. Experimental results demonstrate that the proposed approach is readily deployable on resource-constrained devices and significantly enhances the security, robustness, and scalability of IoT service delivery.

Abstract

As the Internet of Things (IoT) continues its rapid expansion, the attack surface grows accordingly, with emerging threats targeting smart objects and their interactions. In this evolving landscape, securing service provisioning is crucial to ensure the proper functioning, security, and reliability of the IoT ecosystem. Service provisioning encompasses key tasks such as device registration, configuration, authentication, authorization, and software deployment, all of which are essential for seamless and secure IoT operations. In this paper, we present a comprehensive framework designed to select the most suitable smart objects to deliver a target service within a given IoT environment while also monitoring the behavior of the entities involved during the service provisioning phase. To achieve this, we employ a Deep Reinforcement Learning (DRL) approach in which an intelligent agent learns, through interaction with a complex, dynamic environment, how to adapt to changes while adhering to predefined security constraints. For behavioral monitoring, we leverage Federated Learning (FL) to develop a global Behavioral Fingerprinting (BF) model that is fully distributed and can analyze how IoT devices interact within the network. In addition, the BF is used to compute a reliability score for each service provider, reflecting its degree of compliance with the defined security constraints. This score is then incorporated into the service provisioning process, allowing smart objects to select providers not only according to functional suitability but also to their reliability level. Finally, we conduct an extensive experimental evaluation to assess the robustness and scalability of our approach. The results demonstrate that our solution can be effectively deployed even on resource-constrained IoT devices, making it a viable and scalable security-enhancing mechanism for modern IoT ecosystems.

Problem

• IoT security
• service provisioning
• behavioral monitoring
• reliability assessment
• attack surface

Innovation

• Deep Reinforcement Learning
• Federated Learning
• Behavioral Fingerprinting
• Secure Service Provisioning
• IoT Security