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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


Syed Aziz Shah

Shahid Latif

Fawad Ahmed

Zhuo Zou


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.

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
Keywords Cybersecurity, Deep Learning, IIoT, Intrusion Detection, Random Neural Network
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