Cyber Attack Localization in Smart Grids by Graph Modulation (Brief Announcement)

  • Elisabeth DrayerEmail author
  • Tirza Routtenberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11527)


In this brief announcement we present our ongoing work to localize “false data injection” (FDI) attacks on the system state of modern power systems, better known as smart grids. Because of their exceptional importance for our society and together with the increasing presence of information and telecommunication (ICT) components, these power systems are a vulnerable target for cyber attacks. In our method, we represent the power system as a graph and use a generalized modulation operator that is applied on the states of the system. Our preliminary results indicate that attacked grid states exhibit specific modulation patterns that facilitate the localization of the attacks on the particular buses of the grid. This approach is demonstrated by several case study simulations.


False data injection (FDI) attacks Anomaly detection Graph signal processing Laplacian matrix Smart grid 


  1. 1.
    Drayer, E., Routtenberg, T.: Detection of false data injection attacks in smart grids based on graph signal processing. ArXiv e-prints, December 2018
  2. 2.
    Drayer, E., Routtenberg, T.: Intrusion detection in smart grid measurement infrastructures based on principal component analysis. In: Accepted for IEEE PowerTech (2019)Google Scholar
  3. 3.
    Liang, G., Zhao, J., Luo, F., Weller, S.R., Dong, Z.Y.: A review of false data injection attacks against modern power systems. IEEE Trans. Smart Grid 8(4), 1630–1638 (2017). Scholar
  4. 4.
    Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process. Mag. 30(3), 83–98 (2013). Scholar
  5. 5.
    Shuman, D.I., Ricaud, B., Vandergheynst, P.: Vertex-frequency analysis on graphs. Appl. Comput. Harmonic Anal. 40(2), 260–291 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Sridhar, S., Hahn, A., Govindarasu, M.: Cyber-physical system security for the electric power grid. Proc. IEEE 100(1), 210–224 (2012). Scholar
  7. 7.
    Ten, C., Manimaran, G., Liu, C.: Cybersecurity for critical infrastructures: attack and defense modeling. IEEE Trans. Syst. Man Cybern. - Part A: Syst. Hum. 40(4), 853–865 (2010). Scholar
  8. 8.
    Thurner, L., et al.: Pandapower - an open source python tool for convenient modeling, analysis and optimization of electric power systems. IEEE Trans. Power Syst. 33(6), 6510–6521 (2018). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringBen-Gurion University of the NegevBeer ShevaIsrael

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