Deakin University
Browse

File(s) under permanent embargo

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

journal contribution
posted on 2023-03-24, 01:01 authored by Devinder KaurDevinder Kaur, Adnan AnwarAdnan Anwar, Innocent Kamwa, Shama IslamShama Islam, SM 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.

History

Journal

IEEE Access

Volume

11

Pagination

18910-18920

Location

Piscataway, N.J.

ISSN

2169-3536

eISSN

2169-3536

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Usage metrics

    Research Publications

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC