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Phasor measurement unit selection for unobservable electric power data integrity attack detection

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  • Giani, Annarita
  • Bent, Russell
  • Pan, Feng

Abstract

Electric power system operators make critical decisions based on remote measurements. If the measurements are compromised, the decisions made on the basis of the bad measurements could lead to critical consequences. Of particular concern are unobservable attacks where compromised measurements are not flagged as erroneous by bad data detection algorithms. Secure measurement devices, such as phasor measurement units (PMUs), can help to recognize these attacks. This paper presents an algorithm based on integer programming for the optimal placement of PMUs to detect unobservable electric power SCADA data integrity attacks. The algorithm can also be used to identify minimal sets of existing PMUs whose data is needed to detect unobservable bad data attacks. Practical examples drawn from the power engineering literature are used to demonstrate the efficiency of the algorithm.

Suggested Citation

  • Giani, Annarita & Bent, Russell & Pan, Feng, 2014. "Phasor measurement unit selection for unobservable electric power data integrity attack detection," International Journal of Critical Infrastructure Protection, Elsevier, vol. 7(3), pages 155-164.
    • Handle: RePEc:eee:ijocip:v:7:y:2014:i:3:p:155-164
    DOI: 10.1016/j.ijcip.2014.06.001
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    References listed on IDEAS

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    1. Shepard, Daniel P. & Humphreys, Todd E. & Fansler, Aaron A., 2012. "Evaluation of the vulnerability of phasor measurement units to GPS spoofing attacks," International Journal of Critical Infrastructure Protection, Elsevier, vol. 5(3), pages 146-153.
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    Cited by:

    1. Reda, Haftu Tasew & Anwar, Adnan & Mahmood, Abdun, 2022. "Comprehensive survey and taxonomies of false data injection attacks in smart grids: attack models, targets, and impacts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    2. Genge, Béla & Haller, Piroska & Kiss, István, 2016. "A framework for designing resilient distributed intrusion detection systems for critical infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 15(C), pages 3-11.
    3. Genge, Béla & Kiss, István & Haller, Piroska, 2015. "A system dynamics approach for assessing the impact of cyber attacks on critical infrastructures," International Journal of Critical Infrastructure Protection, Elsevier, vol. 10(C), pages 3-17.
    4. Haller, Piroska & Genge, Béla & Duka, Adrian-Vasile, 2019. "On the practical integration of anomaly detection techniques in industrial control applications," International Journal of Critical Infrastructure Protection, Elsevier, vol. 24(C), pages 48-68.
    5. Li, Lei & Wang, Wenting & Ma, Qiang & Pan, Kunpeng & Liu, Xin & Lin, Lin & Li, Jian, 2021. "Cyber attack estimation and detection for cyber-physical power systems," Applied Mathematics and Computation, Elsevier, vol. 400(C).

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