A Deep Learning Approach for Identifying Malicious Activities in the Industrial Internet of Things

Authors

Mohammed Amin Almaiah, King Abdullah the II IT School, The University of Jordan, Amman 11942, JordanFollow
Fuad Ali El-Qirem, Faculty of Architecture and Design, Al-Zaytoonah University of Jordan, Amman 11733, Jordan.
Rami Shehab, Vice-Presidency for Postgraduate Studies and Scientific Research, King Faisal University, Al-Ahsa, Saudi Arabia
Khaled Sulieman Momani, Department of Educational Administration, Faculty of Educational Sciences, Ajloun National University, Ajloun, Jordan

Document Type

Article

Keywords

Industrial Internet of Things (IIoT), Deep Learning, Intrusion Detection Systems (IDS), Cybersecurity, Anomaly Detection

Abstract

Data-driven decision-making, real-time connectivity, and automation have transformed industrial operations with the Industrial Internet of Things. However, the integration also introduces substantial cybersecurity vulnerabilities, making IIoT networks a prime target for malicious activities. Cyber threats are evolving and becoming more sophisticated, which makes traditional security mechanisms inadequate. An approach using deep learning to detect malicious activities in IIoT environments is examined. It is investigated whether Deep Feed Forward neural networks, autoencoders, and convolutional neural networks are effective at detecting anomalies and mitigating cyber threats. NSL-KDD and UNSW-NB15 benchmark datasets are used to evaluate the proposed model's accuracy, precision, and detection rates. In addition to strengthening IIoT security, deep learning techniques can also ensure the resilience of industrial infrastructure.

How to Cite This Article

Almaiah, Mohammed Amin; El-Qirem, Fuad Ali; Shehab, Rami; and Momani, Khaled Sulieman (2025) "A Deep Learning Approach for Identifying Malicious Activities in the Industrial Internet of Things," Mesopotamian Journal of Computer Science: Vol. 5: Iss. 1, Article 7.
DOI: https://doi.org/10.58496/MJCSC/2025/007
Available at: https://map.researchcommons.org/mjcsc/vol5/iss1/7