A Hybrid AI-Blockchain Framework For Securing Industrial Iot Devices

Abstract

The rapid expansion of Industrial Internet of Things (IIoT) networks has introduced significant security vulnerabilities, as traditional authentication methods often prove inadequate (Derrick Lim Kin Yeap et al., 2024). These challenges pose risks to data integrity, confidentiality, and access control within industrial processes that rely on interconnected smart sensors and actuators (Ahamed Aljuhani et al., 2024). Current systems struggle with securing communication, managing device identities, and preventing threats like unauthorized access and data breaches across insecure communication mediums (Ahamed Aljuhani et al., 2024). To address these issues, this paper proposes a novel, comprehensive security framework that integrates blockchain, deep learning, and advanced cryptographic techniques for IIoTenvironments  (Derrick Lim Kin Yeap et al., 2024).This framework introduces a private blockchain-based secure communication mechanism for IIoT entities, utilizing a Proof-of-Authority (PoA) consensus for transaction verification and block creation on cloud servers (Ahamed Aljuhani et al., 2024). Furthermore, it incorporates a deep-learning-based Intrusion Detection System (IDS) that combines a contractive sparse autoencoder (CSAE) with attention-based bidirectional long short-term memory (ABiLSTM) networks for effective cyberattack detection (Ahamed Aljuhani et al., 2024). This integrated approach enhances data integrity through a decentralized ledger, automates security processes via smart contracts, and improves real-time threat response capabilities (Manjushri Joshi et al., 2024). Practical implementation demonstrates significant improvements in communication security, data privacy, and robust defense against evolving cyber threats in IIoT networks (Prabhat Kumar et al., 2023).