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All Journal International Journal of Research and Applied Technology (INJURATECH)
Mohammed , Marwan Aziz
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Deep Learning Security Schemes in IIoT: A Review Askar, Shavan; Hussein, Diana; Ibrahim, Media; Mohammed , Marwan Aziz
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 1 (2025): Vol 5 No 1 (2025)
Publisher : Universitas Komputer Indonesia

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Abstract

The Industrial Internet of Things (IIoT) is a fast-growing technology that might digitize and connect numerous industries for substantial economic prospects and global GDP growth. By the fourth industrial revolution, Industrial Internet of Things (IIoT) platforms create massive, dynamic, and inharmonious data from interconnected devices and sensors. Security and data analysis are complicated by such large diverse data. As IIoT increases, cyberattacks become more diversified and complicated, making anomaly detection algorithms less successful. IIoT is utilized in manufacturing, logistics, transportation, oil and gas, mining, metallurgy, energy utilities, and aviation. IIoT offers significant potential for industrial application development, however cyberattacks and higher security requirements are possible. The enormous volume of data produced by IoT devices demands advanced data analysis and processing technologies like deep learning. Smart assembly, smart manufacturing, efficient networking, and accident detection and prevention are possible with DL algorithms in the Industrial Internet of Things (IIoT). These many applications inspired this article on DL's IIoT potential.
Co-Authors askar, shavan Hussein, Diana Ibrahim, Media