A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things

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 m...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.55595-55605
Main Authors: Huma, Zil E., Latif, Shahid, Ahmad, Jawad, Idrees, Zeba, Ibrar, Anas, Zou, Zhuo, Alqahtani, Fehaid, Baothman, Fatmah
Format: Article
Language:English
Subjects:
Algorithms
Attack detection
Business metrics
cybersecurity
Deep learning
Feature extraction
Industrial applications
Industrial Internet of Things
Industries
Internet of Things
Machine learning
Malware
Multilayer perceptrons
Neural networks
Performance evaluation
Performance measurement
random neural network
Regularization
Security
Servers
Smart buildings
Smart grid
Supply chains
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Summary: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 the Curve (AUC). The HDRaNN classified 16 different types of cyberattacks using with higher accuracy of 98% and 99% for DS2OS and UNSW-NB15, respectively. To measure the effectiveness of the proposed scheme, the performance metrics are also compared with several state-of-the-art attack detection algorithms. The findings of HDRaNN proved its superior performance over other DL-based schemes. The deployment perspective of the proposed work is also highlighted in this work.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3071766