The rapid growth of Internet of Things (IoT) ecosystems has generated substantial industrial progress, yet it has also introduced intricate security and privacy issues. IoT deployments cannot be properly supported with traditional cloud-centric approaches because they require improved bandwidth utilization, reduced latency, and enhanced trust mechanisms. The research proposes Artificial Intelligence-Driven Secure Edge Trust Framework (AI-SET), which establishes a comprehensive edge-based security design that connects network intrusion detection with federated learning capabilities to implement adaptive trust-based access control for IoT system protection. The AI-SET framework comprises three central elements. Real-time anomaly detection at the network edge through the Edge-Resident Intrusion Detection System operates with lightweight AI algorithms to minimize dependency on centralized systems. Privacy-preserving federated learning utilizes the modified FedAvg algorithm, which is supported by differential privacy and homomorphic encryption. Security measures enabled by this model allow algorithms to be trained across decentralized sources that contain heterogeneous and non-identically distributed (non-IID) data. A dynamic access control system utilizes trust assessment models to evaluate device context and behavior for real-time permission evaluations. The framework undergoes validation by running tests with the NAB dataset, supported by Jetson Nano and Raspberry Pi edge devices, and tools including Suricata, Metasploit, and the WAZUH threat platform. Evidence shows that AI-SET boasts higher accuracy in intrusion detection, enhanced communication performance, and superior access control security compared to standard approaches. AI-SET demonstrates immunity against attempted model poisoning attacks and unauthorized system breaches, achieving this protection while maintaining low operational costs and ensuring secure data privacy. The research presents AI-SET as an adaptable, resilient, and sensitive-minded security framework for future IoT systems, through its holistic control of edge intelligence, secure network operations, and automated trust management.
Padmavathi et al. (Tue,) studied this question.