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

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

    Research output: Contribution to journalArticlepeer-review

    10 Citations (Scopus)
    39 Downloads (Pure)

    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 swarm optimization (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.

    Original languageEnglish
    Pages (from-to)8112-8121
    Number of pages10
    JournalJournal of King Saud University - Computer and Information Sciences
    Volume34
    Issue number10
    Early online date2 Aug 2022
    DOIs
    Publication statusPublished - Nov 2022

    Bibliographical note

    This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

    Keywords

    • Cybersecurity
    • Deep learning
    • IIoT
    • Intrusion detection
    • Random neural network

    ASJC Scopus subject areas

    • Computer Science(all)

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