Trust Score Prediction and Management in IoT Ecosystems Using Markov Chains and MADM Techniques

Abstract

The growth of IoT networks necessitates robust and adaptive trust management (TM) systems to ensure secure and reliable interactions between devices. This paper introduces a novel TM framework for IoT devices, leveraging a statistical Markov chain model to calculate dynamic trust scores. Our approach integrates a Multi-Attribute Decision-Making (MADM) methodology to rank devices based on trustworthiness, providing a resource-efficient alternative to machine learning (ML)-based models. In contrast to ML approaches, which often require extensive data and are vulnerable to adversarial attacks, our statistical model provides a resilient and computationally efficient solution suitable for environments with limited data availability. The system architecture combines a Trust Management Server (TMS), an Intrusion Detection System (IDS), and Distributed Ledger Technology (DLT) to secure data integrity and enable real-time trust assessment. Performance evaluations confirm the model’s capacity to manage diverse security threats within IoT ecosystems, while future work will focus on enhancing the system’s adaptability to novel threats, such as zero-day attacks, and exploring alternative decision-making models to improve resilience under uncertainty. This TM approach advances IoT network security by offering a scalable, lightweight solution adaptable to varied IoT environments.