DRaNN_PSO: A deep random neural network with particle swarm optimization for intrusion detection in the industrial internet of things

Ahmad, Jawad; Shah, Syed Aziz; Latif, Shahid; Ahmed, Fawad; Zou, Zhuo; Pitropakis, Nikolaos

Abstract

The Industrial Internet of Things (IIoT) is a rapidly emerging technology that increases the efficiency and productivity of industrial environments by integrating smart sensors and devices with the internet. The advancements in communication technologies have introduced stable connectivity and a higher data transfer rate in the IIoT. The IIoT devices generate a massive amount of information that requires intelligent data processing techniques for the development of cybersecurity mechanisms. In this regard, deep learning (DL) can be an appropriate choice. This paper proposes a Deep Random Neural Network (DRaNN) based fast and reliable attack detection scheme for IIoT environments. The RaNN is an advanced variant of the traditional Artificial Neural Network (ANN) with a highly distributed nature and better generalization capabilities. To attain a higher attack detection accuracy, the proposed RaNN is optimally trained by incorporating hybrid particle swarmoptimization (PSO) with sequential quadratic programming (SQP). The SQP-enabled PSO facilitates the neural network to select optimal hyperparameters. The efficacy of the suggested scheme is analyzed in both binary and multiclass configurations by conducting extensive experiments on three new IIoT datasets. The experimental outcomes demonstrates the promising performance of the proposed design for all datasets.

Citation

Ahmad, J., Shah, S. A., Latif, S., Ahmed, F., Zou, Z., & Pitropakis, N. (2022). DRaNN_PSO: A deep random neural network with particle swarm optimization for intrusion detection in the industrial internet of things. Journal of King Saud University (Computer and Information Sciences), 34(10), 8112-8121. https://doi.org/10.1016/j.jksuci.2022.07.023

• Journal Article Type: Article
• Acceptance Date: Jul 29, 2022
• Online Publication Date: Aug 2, 2022
• Publication Date: 2022-11
• Deposit Date: Aug 10, 2022
• Publicly Available Date: Jun 26, 2023
• Journal: Journal of King Saud University - Computer and Information Sciences
• Print ISSN: 1319-1578
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 34
• Issue: 10
• Pages: 8112-8121
• DOI: https://doi.org/10.1016/j.jksuci.2022.07.023
• Keywords: Cybersecurity, Deep Learning, IIoT, Intrusion Detection, Random Neural Network
• Public URL: http://researchrepository.napier.ac.uk/Output/2895729