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Title
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A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things
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Sub-Title |
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Subject |
Attack detection, cybersecurity, deep learning, Industrial Internet of Things, random neural network.
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Author |
ZIL E. HUMA , SHAHID LATIF, JAWAD AHMAD , ZEBA ID
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Publish Year |
2021 |
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Supervisor |
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Diss#. |
10.1109/ACCESS.2021.3071766 |
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Pages |
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Text Language |
English |
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Accession |
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Library Section |
Research Article |
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Abstract |
The Industrial Internet of Things (IIoT) refers to the use of traditional Internet of Things (IoT)
concepts in industrial sectors and applications. IIoT has several applications in smart homes, smart cities,
smart grids, connected cars, and supply chain management. However, these systems are being more
frequently targeted by cybercriminals. Deep learning and big data analytics have great potential in designing
and developing robust security mechanisms for IIoT networks. In this paper, a novel hybrid deep random
neural network (HDRaNN) for cyberattack detection in the IIoT is presented. The HDRaNN combines a deep
random neural network and a multilayer perceptron with dropout regularization. The proposed technique is
evaluated using two IIoT security-related datasets: (i) DS2OS and (ii) UNSW-NB15. The performance of
the proposed scheme is analyzed through a number of performance metrics such as accuracy, precision,
recall, F1 score, log loss, Region of Convergence (ROC), and Area Under t
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