Blockchain-Integrated AI for Secure Data Transmission in IoT Ecosystems

Abstract

The rapid proliferation of Internet of Things (IoT) devices across critical sectors has introduced unprecedented security challenges. While Artificial Intelligence (AI) provides powerful tools for threat detection, AI models themselves are vulnerable to data poisoning and adversarial attacks. This paper introduces a novel hybrid framework that synergistically integrates AI with blockchain technology to create a secure and resilient data transmission ecosystem for the IoT. Our architecture utilizes AI-driven techniques, including simulated federated learning, for privacy-preserving anomaly detection at the network edge. A blockchain-based ledger then provides tamper-resistant data validation and immutable record-keeping. The system employs smart contracts on a private Ethereum testnet, combined with the InterPlanetary File System (IPFS) for efficient off-chain storage, ensuring both data integrity and provenance. We evaluated the framework in a simulated environment using benchmark datasets, including CICIDS2017 and UNSW-NB15. The results show a high efficacy in detecting sophisticated attacks, with a Random Forest classifier achieving 98.46