Detecting network intrusions in cyber-physical systems using deep autoencoder-based dimensionality reduction approach anddeep neural networks

Authors

  • A. E. Ibor
    Department of Computer Science, University of Calabar, Calabar, Nigeria
    https://orcid.org/0000-0002-4083-454X
  • D. O. Egete
    Department of Computer Science, University of Calabar, Calabar, Nigeria
  • A. O. Otiko
    Department of Computer Science, University of Cross River State, Calabar, Nigeria
  • D. U. Ashishie
    Department of Computer Science, University of Calabar, Calabar, Nigeria

Keywords:

Adversarial attacks, Deep autoencoder, Deep learning, Intrusion detection, Cyber-physical systems

Abstract

Cyber-Physical Systems (CPSs) that integrate computational and physical processes are the foundation of reliability in prominent areas of critical infrastructure, including transportation, energy, and manufacturing. The expansion in connected CPSs has made them vulnerable to various and changing intrusions into their networks. This research proposes a hybrid deep learning architecture that integrates the utilisation of a denoising autoencoder as a feature dimensionality reduction component with a five-layer deep feedforward neural network as an effective intrusion classifier. The model is trained and tested on CICIDS2017 and UNSW-NB15 datasets with a rich collection of attack patterns such as DoS, DDoS, Shellcode, and Worm attacks. The denoising autoencoder effectively learns higher-level representations of network traffic data, whereas the deep feedforward network facilitates precise multi-class classification. Empirical results demonstrate that the model achieves 99.99% and 99.95% detection accuracies on CICIDS2017 and UNSW-NB15 datasets, respectively, at very low false positive rates. Comparative analysis with state-of-the-art techniques further confirms the superior performance and generalisability of the presented solution, highlighting its applicability to real-time CPS threat detection systems.

Author Biography

A. E. Ibor

Senior Lecturer in Computer Science 

Department of Computer Science 

 

Dimensions

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Architecture of the proposed intrusion detection model.

Published

2025-08-01

How to Cite

Detecting network intrusions in cyber-physical systems using deep autoencoder-based dimensionality reduction approach anddeep neural networks. (2025). Journal of the Nigerian Society of Physical Sciences, 7(3), 2689. https://doi.org/10.46481/jnsps.2025.2689

Issue

Volume 7, Issue 3, August 2025

Section

Computer Science
Copyright & Licensing

Copyright (c) 2025 A. E. Ibor, D. O. Egete, A. O. Otiko, D. U. Ashishie

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Detecting network intrusions in cyber-physical systems using deep autoencoder-based dimensionality reduction approach anddeep neural networks. (2025). Journal of the Nigerian Society of Physical Sciences, 7(3), 2689. https://doi.org/10.46481/jnsps.2025.2689

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