Securing Industrial IoT Environments through Machine Learning-Based Anomaly Detection in the Age of Pervasive Connectivity

Authors

  • Bassam Mohammad Elzaghmouri Department of Computer Science .Faculty of Information technology, Jerash University, Jerash, Jordan
  • Ahmad Khader Habboush Faculty of Information Technology, Jerash University, Jerash, Jordan
  • Marwan Abu-Zanona Department of Management Information Systems, College of Business Administration, King Faisal University, Saudi Arabia
  • Suprava Ranjan Laha Department of Computer Science and Engineering, Institute of technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
  • Binod Kumar Pattanayak Department of Computer Science and Engineering, Institute of technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
  • Saumendra Pattnaik Department of Computer Science and Engineering, Institute of technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
  • Bibhuprasad Mohanty Department of Electronics and Communication Engineering, Institute of technical Education and Research, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha, India

Keywords:

Industrial IoT, Anomaly detection, Security, Deep Learning, Risk mitigation

Abstract

In an era characterized by the relentless evolution of Internet of Things (IoT) technologies, marked by the pervasive adoption of smart devices and the ever-expanding realm of Internet connectivity, the IoT has seamlessly integrated itself into our daily lives. This integration has ushered in a new era for manufacturing companies, enabling them to conduct real-time monitoring of their machinery, supervise product quality, and closely monitor environmental variables within their facilities. In addition to the immediate benefits of risk mitigation and loss prevention, this multifaceted approach has provided decision-makers with a comprehensive perspective for making informed decisions. People are now more dependent than ever in IoT devices and services. However, anomalies within IoT networks pose a critical concern despite the IoT's immense potential. These anomalies can pose significant security and safety risks if they go undetected. Identifying and alerting users of these anomalies on time has become crucial for preventing potential damages and losses. In response to this imperative, our research endeavors to utilize the power of Machine Learning and Deep Learning techniques to detect anomalies in IoT networks. We undertake exhaustive experiments with the IoT-23 dataset to validate our methodology empirically. Our research examines an exhaustive comparison of numerous models, assessing their performance and time efficiency to determine the optimal algorithm for achieving high detection accuracy under strict time constraints. This research represents an important step towards enhancing the security of Industrial IoT environments, thereby protecting vital infrastructure and ensuring the integrity of industrial operations in our increasingly interconnected world.

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References

Stoian, N. A. (2020). Machine learning for anomaly detection in IoT networks: Malware analysis on the iot-23 data set (Bachelor's thesis, University of Twente).

Liang, Y., & Vankayalapati, N. Machine Learning and Deep Learning Methods for Better Anomaly Detection in IoT-23 Dataset Cybersecurity. Preprint.

Laha, S. R., Pattanayak, B. K., & Pattnaik, S. (2022). Advancement of Environmental Monitoring System Using IoT and Sensor: A Comprehensive Analysis. AIMS Environmental Science, 9(6), 771-800.

Pattnaik, S., Banerjee, S., Laha, S. R., Pattanayak, B. K., & Sahu, G. P. (2022). A Novel Intelligent Street Light Control System Using IoT. In Intelligent and Cloud Computing: Proceedings of ICICC 2021 (pp. 145-156). Singapore: Springer Nature Singapore.

Mahapatra, S. K., Pattanayak, B. K., Pati, B., Laha, S. R., Pattnaik, S., & Mohanty, B. (2023). An IoT Based Novel Hybrid-Gamified Educational Approach to Enhance Student’s Learning Ability. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 374-393.

Laha, S. R., Mahapatra, S. K., Pattnaik, S., Pattanayak, B. K., & Pati, B. (2021). U-INS: an android-based navigation system. In Cognitive Informatics and Soft Computing: Proceeding of CISC 2020 (pp. 125-132). Springer Singapore.

Biswal, A. K., Singh, D., Pattanayak, B. K., Samanta, D., Chaudhry, S. A., & Irshad, A. (2021). Adaptive fault-tolerant system and optimal power allocation for smart vehicles in smart cities using controller area network. Security and Communication Networks, 2021, 1-13.

Hosenkhan, M. R., & Pattanayak, B. K. (2020). Security issues in internet of things (IoT): a comprehensive review. New Paradigm in Decision Science and Management: Proceedings of ICDSM 2018, 359-369.

Mohi-Ud-Din, G., Zheng, J., Liu, Z., Asim, M., Chen, J., Liu, J., & Lin, Z. (2022, October). NIDS: Random Forest Based Novel Network Intrusion Detection System for Enhanced Cybersecurity in VANET's. In 2022 International Conference on Virtual Reality, Human-Computer Interaction and Artificial Intelligence (VRHCIAI) (pp. 255-260). IEEE.

Sarker, I. H., Khan, A. I., Abushark, Y. B., & Alsolami, F. (2022). Internet of things (IoT) security intelligence: a comprehensive overview, machine learning solutions and research directions. Mobile Networks and Applications, 1-17.

Sarker, I. H. (2022). Machine learning for intelligent data analysis and automation in cybersecurity: current and future prospects. Annals of Data Science, 1-26.

Wu, B., Liu, S., Feng, R., Xie, X., Siow, J., & Lin, S. W. (2022). Enhancing security patch identification by capturing structures in commits. IEEE Transactions on Dependable and Secure Computing.

Thom, J., Thom, N., Sengupta, S., & Hand, E. (2022, January). Smart recon: Network traffic fingerprinting for IoT device identification. In 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0072-0079). IEEE.

Dushyant, K., Muskan, G., Annu, Gupta, A., & Pramanik, S. (2022). Utilizing Machine Learning and Deep Learning in Cybesecurity: An Innovative Approach. Cyber Security and Digital Forensics, 271-293.

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Published

25.12.2023

How to Cite

Elzaghmouri, B. M. ., Habboush, A. K. ., Abu-Zanona , M. ., Laha, S. R. ., Pattanayak, B. K. ., Pattnaik, S. ., & Mohanty, B. . (2023). Securing Industrial IoT Environments through Machine Learning-Based Anomaly Detection in the Age of Pervasive Connectivity. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 733–740. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4316

Issue

Vol. 12 No. 2 (2024)

Section

Research Article

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