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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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    Date
    2023-02-22
    Author
    Kaur, Devinder
    Anwar, Adnan
    Kamwa, Innocent
    Islam, Shama
    Muyeen, S. M.
    Hosseinzadeh, Nasser
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    Abstract
    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.
    URI
    https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85149417787&origin=inward
    DOI/handle
    http://dx.doi.org/10.1109/ACCESS.2023.3247947
    http://hdl.handle.net/10576/61937
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    • Electrical Engineering [‎2821‎ items ]

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