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A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems

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submitted on 2024-02-12, 08:07 and posted on 2024-02-12, 08:08 authored by Devinder Kaur, Adnan Anwar, Innocent Kamwa, Shama Islam, S. M. Muyeen, Nasser Hosseinzadeh

The emergence of cyber-physical smart grid (CPSG) systems has revolutionized the traditional power grid by enabling the bidirectional energy flow between consumers and utilities. However, due to escalated information exchange between the end-users, it has posed a greater challenge to the cyber security mechanisms for the communication networks at the cyber and physical planes. To address these challenges, we propose a Bayesian approach integrated with deep convolutional neural networks (CNN-Bayesian). While, the Bayesian component is used to discriminate cyber-physical intrusions from the normal events in the binary and multi-class events. CNN layers are utilized to handle the high-dimensional feature space prior to the intrusions classification task. The proposed method is validated using real-time Industrial control systems (ICS) dataset against the standard deep learning-based classification methods such as recurrent neural networks (RNN) and long-short term memory (LSTM). From the experimental results, it can be inferred that the proposed CNN-Bayesian method outperforms the existing benchmark classification methods to discriminate intrusions in CPSG systems using evaluation metrics such as accuracy, precision, recall, and F1 -score.

Other Information

Published in: IEEE Access
License: http://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1109/access.2023.3247947

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

IEEE

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Qatar University
  • College of Engineering - QU