International Journal of Multidisciplinary Engineering Sciences
Peer Reviewed Open Access Journal
Peer Reviewed Open Access Journal
Industrial Internet of Things (IIoT) technologies have transformed modern industrial environments by enabling intelligent automation, real-time monitoring, predictive maintenance, and smart manufacturing operations. However, the rapid adoption of interconnected industrial devices has introduced significant cybersecurity challenges including unauthorized access, ransomware attacks, data breaches, distributed denial-of-service attacks, and network intrusions. Traditional security mechanisms are insufficient to protect large-scale IIoT infrastructures due to device heterogeneity, limited computational capabilities, and dynamic industrial environments. This paper proposes an Artificial Intelligence-based Industrial IoT security framework integrating machine learning and blockchain technologies for secure industrial communication and intelligent threat detection. The proposed framework employs IoT sensors, edge computing, cloud infrastructure, and AI-driven intrusion detection models to identify malicious activities in industrial networks. Blockchain technology is incorporated to ensure secure data sharing, authentication, and tamper-resistant communication among IIoT devices. Experimental analysis demonstrates improved detection accuracy, reduced false-positive rates, enhanced network security, and efficient real-time monitoring compared with conventional IIoT security approaches. The proposed system offers a scalable and intelligent solution suitable for Industry 4.0 and smart manufacturing environments.
Industrial Internet of Things, Artificial Intelligence, Machine Learning, Blockchain, Cybersecurity, Intrusion Detection, Smart Manufacturing, Industry 4.0
Alotaibi, B. (2023). A survey on industrial Internet of Things security: Requirements, attacks, AI-based solutions, and edge computing opportunities. Sensors, 23(17), 1–35.
Shawkat, M., et al. (2025). Blockchain and federated learning based on aggregation techniques for industrial IoT: A contemporary survey. Peer-to-Peer Networking and Applications, 18, 1–28.
Rahman, A., et al. (2024). Blockchain-based AI methods for managing industrial IoT: Recent developments, integration challenges and opportunities. arXiv preprint.
Kumar, S., & Sharma, R. (2024). Machine learning enabled Industrial IoT security: Challenges, trends and solutions. Journal of Industrial Information Integration, 38, 100549.
Chen, Y., et al. (2024). Blockchain-enabled intrusion detection approaches for edge-enabled industrial IoT networks. Ad Hoc Networks, 152, 103320.
Lee, H., & Kim, J. (2022). Industrial cybersecurity in smart manufacturing systems. IEEE Access, 10, 45122–45138.
Rahman, M., et al. (2023). Secure communication models in Industry 4.0. Future Generation Computer Systems, 142, 115–129.
Patel, A., & Singh, V. (2024). Cyberattack detection in Industrial IoT using machine learning. Computers & Security, 128, 103–117.
Hussain, F., et al. (2023). Machine learning in IoT security: Current solutions and future challenges. IEEE Communications Surveys & Tutorials, 21(4), 1–25.
Al-Garadi, M., et al. (2022). A survey of machine and deep learning methods for Internet of Things security. IEEE Communications Surveys & Tutorials, 22(3), 1646–1685.
Ahmed, R., et al. (2024). Deep learning-based intrusion detection for IIoT systems. IEEE Internet of Things Journal, 11(2), 2110–2125.
Chen, X., et al. (2024). Blockchain-enabled authentication mechanisms for smart industries. IEEE Access, 12, 22110–22130.
Li, Y., & Wang, P. (2025). Secure blockchain communication in industrial cyber-physical systems. Journal of Network and Computer Applications, 219, 103–118.
Alotaibi, B. (2023). Industrial IoT security requirements and AI opportunities. Sensors, 23(17), 7470.
Kumar, S., et al. (2024). AI-enabled intrusion detection framework for industrial networks. Expert Systems with Applications, 241, 122–138.
Rahman, A., et al. (2024). Blockchain security framework for Industrial IoT systems. Internet of Things, 26, 101187.
Sharma, D., & Gupta, N. (2025). Deep learning-assisted anomaly detection in IIoT. Applied Soft Computing, 148, 110–126.
Chen, X., et al. (2025). Smart contract-based authentication for Industry 4.0. IEEE Transactions on Industrial Informatics, 20(1), 88–101.
Dritsas, E., & Trigka, M. (2024). Machine learning for blockchain and IoT systems in smart cities: A survey. Future Internet, 16(9), 324.
Verma, S., & Roy, P. (2024). Cross-layer secure and energy-efficient IoT frameworks. Sensors, 24(22), 7209.
