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False Data Injection Attack Detection in Smart Grid Using Energy Consumption Forecasting

Author

Listed:
  • Abrar Mahi-al-rashid

    (Department of Mechanical and Production Engineering, Islamic University of Technology, K B Bazar Rd., Gazipur 1704, Bangladesh)

  • Fahmid Hossain

    (Department of Mechanical and Production Engineering, Islamic University of Technology, K B Bazar Rd., Gazipur 1704, Bangladesh)

  • Adnan Anwar

    (School of IT, Deakin University, 75 Pigdons Rd., Waurn Ponds, Geelong 3216, Australia)

  • Sami Azam

    (College of Engineering, IT and Environment, Charles Darwin University, Casuarina 0810, Australia)

Abstract

Supervisory Control and Data Acquisition (SCADA) systems are essential for reliable communication and control of smart grids. However, in the cyber-physical realm, it becomes highly vulnerable to cyber-attacks like False Data Injection (FDI) into the measurement signal which can circumvent the conventional detection methods and interfere with the normal operation of grids, which in turn could potentially lead to huge financial losses and can have a large impact on public safety. It is imperative to have an accurate state estimation of power consumption for further operational decision-making.This work presents novel forecasting-aided anomaly detection using an CNN-LSTM based auto-encoder sequence to sequence architecture to combat against false data injection attacks. We further present an adaptive optimal threshold based on the consumption patterns to identify abnormal behaviour. Evaluation is performed on real-time energy demand consumption data collected from the Australian Energy Market Operator. An extensive experiment shows that the proposed model outperforms other benchmark algorithms in not only improving the data injection attack (95.43%) but also significantly reducing the false positive rate.

Suggested Citation

  • Abrar Mahi-al-rashid & Fahmid Hossain & Adnan Anwar & Sami Azam, 2022. "False Data Injection Attack Detection in Smart Grid Using Energy Consumption Forecasting," Energies, MDPI, vol. 15(13), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4877-:d:854618
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    References listed on IDEAS

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    Cited by:

    1. Zhu, Tianci & Wang, Jun & Zhu, Yonghai & Chen, Haoran & Zhang, Hang & Yin, Shanshan, 2024. "Power grid network security: A lightweight detection model for composite false data injection attacks using spatiotemporal features," International Journal of Critical Infrastructure Protection, Elsevier, vol. 46(C).
    2. Muhammad Waseem & Muhammad Adnan Khan & Arman Goudarzi & Shah Fahad & Intisar Ali Sajjad & Pierluigi Siano, 2023. "Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges," Energies, MDPI, vol. 16(2), pages 1-29, January.
    3. Bartłomiej Gawin & Robert Małkowski & Robert Rink, 2023. "Will NILM Technology Replace Multi-Meter Telemetry Systems for Monitoring Electricity Consumption?," Energies, MDPI, vol. 16(5), pages 1-26, February.

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