Advanced Monitoring Based Intrusion Detection System for Distributed and Intelligent Energy Theft: DIET Attack in Advanced Metering Infrastructure

  • Manali ChakrabortyEmail author
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10730)


Power grid and energy theft has an eternal relationship. Though we moved towards Smart Grid, with an expectation for a more efficient, reliable and secure service, so does the attackers. Smart Grid and AMI systems incorporate a good number of security measures, still it is open to various threats. Recent attacks on Smart Grids in U.S., Gulf State and Ukraine proved that the attacks on the grid have become more sophisticated. In this paper we have introduced a new, distributed and intelligent energy theft: DIET attack and proposed an advanced Intrusion Detection System to protect AMI system. The proposed IDS can perform a passive monitoring on the system as well as detect attackers. This features make this IDS more robust and reliable.


Intrusion detection system Non technical loss Energy theft AMI Smart Grid Trust 



This work is a part of the Ph.D. work of the author, a Senior Research Fellow of Council of Scientific & Industrial Research (CSIR), Government of India. We would like to acknowledge CSIR, for providing the support required for carrying out the research work.


  1. 1.
    Jiang, R., Lu, R., Wang, Y., Luo, J., Shen, C., Shen, X.S.: Energy-theft detection issues for advanced metering infrastructure in smart grid. Tsinghua Sci. Technol. 19(2), 105–120 (2014)CrossRefGoogle Scholar
  2. 2.
    Grochocki, D., Huh, J.H., Berthier, R., Bobba, R., Sanders, W.H., Crdenas, A.A., Jetcheva, J.G.: AMI threats, intrusion detection requirements and deployment recommendations. In: Smart Grid Communications (SmartGridComm), pp. 395–400. IEEE (2012)Google Scholar
  3. 3.
    McLaughlin, S., Podkuiko, D., McDaniel, P.: Energy theft in the advanced metering infrastructure. In: Rome, E., Bloomfield, R. (eds.) CRITIS 2009. LNCS, vol. 6027, pp. 176–187. Springer, Heidelberg (2010). CrossRefGoogle Scholar
  4. 4.
    ABB Inc.: Energy Efficiency in the Power Grid. ABB Inc., Fort Smith (2007)Google Scholar
  5. 5.
    U.S. Energy Information Administration.
  6. 6.
    Chakraborty, M., Deb, N., Chaki, N.: POMSec: Pseudo-opportunistic, multipath secured routing protocol for communications in smart grid. In: Saeed, K., Homenda, W., Chaki, R. (eds.) CISIM 2017. LNCS, vol. 10244, pp. 264–276. Springer, Cham (2017). CrossRefGoogle Scholar
  7. 7.
    Chakraborty, M., Chaki, N.: An IPv6 based hierarchical address configuration scheme for smart grid. In: Applications and Innovations in Mobile Computing (AIMoC), Kolkata, pp. 109–116 (2015).
  8. 8.
    Nagi, J., Yap, K.S., Tiong, S.K., Ahmed, S.K., Mohamad, M.: Nontechnical loss detection for metered customers in power utility using support vector machines. IEEE Trans. Power Deliv. 25(2), 1162–1171 (2010)CrossRefGoogle Scholar
  9. 9.
    Depuru, S., Wang, L., Devabhaktuni, V.: Support vector machine based data classification for detection of electricity theft. In: IEEE/PES Power Systems Conference and Exposition (PSCE), pp. 1–8 (2011)Google Scholar
  10. 10.
    Depuru, S., Wang, L., Devabhaktuni, V., Green, R.C.: High performance computing for detection of electricity theft. Int. J. Electr. Power Energy Syst. 47, 21–30 (2013)CrossRefGoogle Scholar
  11. 11.
    McLaughlin, S., Holbert, B., Zonouz, S., Berthier, R.: AMIDS: A multi-sensor energy theft detection framework for advanced metering infrastructures. In: IEEE Third International Conference on Smart Grid Communications (SmartGridComm), pp. 354–359 (2012)Google Scholar
  12. 12.
    Khoo, B., Cheng, Y.: Using RFID for anti-theft in a Chinese electrical supply company: A cost-benefit analysis. In: IEEE Wireless Telecommunications Symposium (WTS), pp. 1–6 (2011)Google Scholar
  13. 13.
    Xiao, Z., Xiao, Y., Du, D.H.C.: Non-repudiation in neighborhood area networks for smart grid. IEEE Commun. Mag. 51(1), 18–26 (2013)CrossRefGoogle Scholar
  14. 14.
    Amin, S., Schwartz, G.A., Tembine, H.: Incentives and security in electricity distribution networks. In: Grossklags, J., Walrand, J. (eds.) GameSec 2012. LNCS, vol. 7638, pp. 264–280. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  15. 15.
    Cardenas, A.A., Amin, S., Schwartz, G., Dong, R., Sastry, S.: A game theory model for electricity theft detection and privacy-aware control in AMI systems. In: IEEE 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton), pp. 1830–1837 (2012)Google Scholar
  16. 16.
    QualNet 5.2 Simulator: Scalable network technologies Inc.Google Scholar
  17. 17.
    Kim, M.: A survey on guaranteeing availability in smart grid communications. In: Advanced Communication Technology (ICACT), pp. 314–317 (2012)Google Scholar
  18. 18.
    Stallings, W.: Cryptography and Network Security: Principles and Practice, 5th edn. Prentice Hall Press, Upper Saddle River (2010). ISBN: 0136097049 9780136097044Google Scholar
  19. 19.
    Mashima, D., Cárdenas, A.A.: Evaluating electricity theft detectors in smart grid networks. In: Balzarotti, D., Stolfo, S.J., Cova, M. (eds.) RAID 2012. LNCS, vol. 7462, pp. 210–229. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  20. 20.
    Jokar, P., Arianpoo, N., Leung, V.C.M.: Electricity theft detection in AMI using customers consumption patterns. IEEE Trans. Smart Grid 7(1), 216–226 (2016)CrossRefGoogle Scholar
  21. 21.
    Ruppe, R., Griswald, S., Walsh, P., Martin, R.: Near Term Digital Radio (NTDR) system. In: MILCOM 1997, pp. 1282–1287 (1997)Google Scholar
  22. 22.
    Berthier, R., Sanders, W.H.: Specification-based intrusion detection for advanced metering infrastructures. In: IEEE 17th Pacific Rim International Symposium on Dependable Computing, pp. 184–193 (2011)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of CalcuttaKolkataIndia

Personalised recommendations