A POST-QUANTUM SECURE BLOCKCHAIN–FEDERATED LEARNING FRAMEWORK FOR INDUSTRIAL IOT SECURITY

Industrial IoT (IIoT) systems (e.g. smart factories and grids) face rapidly evolving cyber threats, motivating advanced solutions that blend emerging technologies. In this article a novel security framework is proposed that integrates federated learning (for distributed anomaly detection), blockchain (for tamper-resistant trust), and post-quantum cryptographic mechanisms to protect IIoT data and control systems. The framework is motivated by recent analyses showing the effectiveness of AI-driven anomaly detectors for control systems [1] [2], and by the demonstrated threats of quantum computers to classical encryption in blockchains [4]. The system architecture (including a quantum-safe blockchain with a consensus algorithm and signature scheme) is outlined, and the implementation strategies on realistic IIoT devices are described. A case study on a smart manufacturing scenario is included, with experimental or comparative results showing improved accuracy and resilience versus baseline schemes. This work offers researchers and practitioners a detailed, applied approach to IIoT security by combining federated AI, blockchain integrity, and quantum-resistant cryptography.