• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Journal articles
    • View Item
    • Home
    • Griffith Research Online
    • Journal articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Smart Grid Security Enhancement by Using Belief Propagation

    Author(s)
    Amin, BMR
    Taghizadeh, S
    Maric, S
    Hossain, MJ
    Abbas, R
    Griffith University Author(s)
    Taghizadeh, Foad
    Hossain, Jahangir
    Year published
    2021
    Metadata
    Show full item record
    Abstract
    False data injection attack (FDIA) is a critical cyber-attack that can cause disrupt operations and subsequently blackouts in smart grid networks. Cleverly constructed stealthy false measurement vectors can circumvent the bad data detector unit and mislead the state estimation process. This article proposes a novel belief propagation (BP)-based algorithm to detect both random and stealthy-type FDIAs in smart grids with higher detection rate than the state-of-the-art machine learning classifiers such as Naive Bayes, support vector machines, Random Forest, OneR, and AdaBoost. Another novel feature of the proposed algorithm is ...
    View more >
    False data injection attack (FDIA) is a critical cyber-attack that can cause disrupt operations and subsequently blackouts in smart grid networks. Cleverly constructed stealthy false measurement vectors can circumvent the bad data detector unit and mislead the state estimation process. This article proposes a novel belief propagation (BP)-based algorithm to detect both random and stealthy-type FDIAs in smart grids with higher detection rate than the state-of-the-art machine learning classifiers such as Naive Bayes, support vector machines, Random Forest, OneR, and AdaBoost. Another novel feature of the proposed algorithm is to detect FDIAs without using any historical cyber-attack data, which are sketchy due to security constraints and infinitesimal in occurrence numbers. The proposed BP method utilizes local sensor measurement data to calculate local belief and send it as a message signal to the control center. Then, the control center determines final/global belief and compares the result with a predefined threshold value derived from the uncompromised measurement database. The real-time steady-state load data are utilized for dc state estimation. From the obtained results, performance parameters such as detection rate, receiver operating characteristic curve, precision, recall, and F-measure of the proposed BP algorithm are found superior to the aforementioned state-of-the-art machine learning algorithms.
    View less >
    Journal Title
    IEEE Systems Journal
    Volume
    15
    Issue
    2
    DOI
    https://doi.org/10.1109/JSYST.2020.3001951
    Subject
    Electrical engineering
    Electronics, sensors and digital hardware
    Publication URI
    http://hdl.handle.net/10072/409391
    Collection
    • Journal articles

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander