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Attack Context Embedded Data Driven Trust Diagnostics in Smart Metering Infrastructure

Published:21 January 2021Publication History
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Abstract

Spurious power consumption data reported from compromised meters controlled by organized adversaries in the Advanced Metering Infrastructure (AMI) may have drastic consequences on a smart grid’s operations. While existing research on data falsification in smart grids mostly defends against isolated electricity theft, we introduce a taxonomy of various data falsification attack types, when smart meters are compromised by organized or strategic rivals. To counter these attacks, we first propose a coarse-grained and a fine-grained anomaly-based security event detection technique that uses indicators such as deviation and directional change in the time series of the proposed anomaly detection metrics to indicate: (i) occurrence, (ii) type of attack, and (iii) attack strategy used, collectively known asattack context. Leveraging the attack context information, we propose three attack response metrics to the inferred attack context: (a) an unbiased mean indicating a robust location parameter; (b) a median absolute deviation indicating a robust scale parameter; and (c) an attack probability time ratio metric indicating the active time horizon of attacks. Subsequently, we propose a trust scoring model based on Kullback-Leibler (KL) divergence, that embeds the appropriate unbiased mean, the median absolute deviation, and the attack probability ratio metric at runtime to produce trust scores for each smart meter. These trust scores help classify compromised smart meters from the non-compromised ones. The embedding of the attack context, into the trust scoring model, facilitates accurate and rapid classification of compromised meters, even under large fractions of compromised meters, generalize across various attack strategies and margins of false data. Using real datasets collected from two different AMIs, experimental results show that our proposed framework has a high true positive detection rate, while the average false alarm and missed detection rates are much lesser than 10% for most attack combinations for two different real AMI micro-grid datasets. Finally, we also establish fundamental theoretical limits of the proposed method, which will help assess the applicability of our method to other domains.

References

  1. V. Agate, A. Khamesi, S. Gaglio, and S. Silvestri. 2020. Enabling peer-to-peer user-preference-aware energy sharing through reinforcement learning. IEEE International Conference on Communications (ICC).Google ScholarGoogle Scholar
  2. S. Bhattacharjee, A. Thakur, S. Silvestri, and S. K. Das. 2017. Statistical security incident forensics against data falsification in smart grid advanced metering infrastructure. ACM Conference on Data and Application Security (ACM CODASPY). 35--45.Google ScholarGoogle Scholar
  3. S. Bhattacharjee and S. K. Das. 2018. Detection and forensics under stealthy data falsification in smart metering infrastructure. IEEE Trans. on Dependable and Secure Computing, Vol. 16.Google ScholarGoogle Scholar
  4. S. Bhattacharjee, A. Thakur, and S. K. Das. 2018. Towards fast and semi-supervised identification of smart meters launching data falsification attacks. ACM Asia Conference on Computer and Communications Security (ACM ASIACCS). 173--185.Google ScholarGoogle Scholar
  5. P. Box and D. Cox. 1964. An analysis of transformations. Journal of the Royal Statistical Society, Series B. 26, 2 (1964), 211--252.Google ScholarGoogle ScholarCross RefCross Ref
  6. A. Cardenas, R. Berthier, R. Bobba, J. Huh, J. Jetcheva, D. Grochocki, and W. Sanders. 2014. A framework for evaluating intrusion detection architectures in advanced metering infrastructures. IEEE Trans. on Smart Grid 5, 2 (2014), 906--915.Google ScholarGoogle ScholarCross RefCross Ref
  7. V. Chandola, A. Banerjee, and V. Kumar. 2009. Anomaly detection: A survey. ACM Computing Surveys 41, 15 (2009), 15--58.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. S. Ciavarella, J. Y. Joo, and S. Silvestri. 2016. Managing contingencies in smart grids via the internet of things. IEEE Trans. on Smart Grid 7, 4 (2016), 2134--2141.Google ScholarGoogle ScholarCross RefCross Ref
  9. V. Dolce, C. Jackson, S. Silvestri, D. Baker, and A. De Paola. 2018. Social-behavioral aware optimization of energy consumption in smart homes. IEEE International Conf. on Distributed Computing in Sensor Systems (DCOSS), 2018.Google ScholarGoogle Scholar
  10. Y. Ishimaki, S. Bhattacharjee, H. Yamana, and S. K. Das. 2020. Towards privacy-preserving anomaly-based attack detection against data falsification in smart grid. IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SMARTGRIDCOMM), Nov. 2020.Google ScholarGoogle Scholar
  11. R. Jiang, R. Lu, Y. Wang, J. Luo, C. Shen, and X. Shen. 2014. Energy-Theft detection issues for advanced metering infrastructure in smart grids. Tsinghua Science and Technology 19, 2 (2014), 105--120.Google ScholarGoogle ScholarCross RefCross Ref
  12. P. Jokar, N. Arianpoo, and V. Leung. 2016. Electricity theft detection in AMI using customers’ consumption patterns. IEEE Trans. on Smart Grid 7, 1 (2016), 216--226.Google ScholarGoogle ScholarCross RefCross Ref
  13. A. Khamesi, S. Silvestri, D. Baker, and A. De Paola. 2020. Perceived-value driven optimization of energy consumption in smart homes. ACM Transactions on Internet of Things 1, 2 (2020).Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. A. R. Khamesi and S. Silvestri. 2020. Reverse auction-based demand response program: A truthful mutually beneficial mechanism. IEEE Mobile Ad Hoc Sensor and Smart Systems (IEEE MASS) (2020).Google ScholarGoogle Scholar
  15. T. Koppel. 2015. Lights Out: A Cyberattack, A Nation Unprepared, Surviving the Aftermath. Crown Publishers, New York.Google ScholarGoogle Scholar
  16. V. B. Krishna, K. Lee, G. A. Weaver, R. K. Iyer, and W. H. Sanders. 2016. F-DETA: A framework for detecting electricity theft attacks in smart grids. IEEE/IFIP on Dependable Systems and Networks (IEEE DSN). 407--418.Google ScholarGoogle Scholar
  17. S. McLaughlin, D. Podkuiko, and P. McDaniel. 2009. Energy theft in the advanced metering infrastructure. Proc. of Critical Information Infrastructures Security. Springer-Verlag, 176--187.Google ScholarGoogle Scholar
  18. S. McLaughlin, B. Holbert, S. Zonouz, and R. Berthier. 2012. AMIDS: A multi-sensor energy theft detection framework for advanced metering infrastructures. IEEE Conf. on Communications, Control, and Computing Technologies for Smart Grid Communications (SMARTGRIDCOMM). 354--359.Google ScholarGoogle Scholar
  19. D. Mashima and A. Alvaro. 2012. Evaluating electricity theft detectors in smart grid networks. Springer Intl. Workshop on Recent Advances in Intrusion Detection. 210--229, Sept. 2012.Google ScholarGoogle Scholar
  20. R. Mohassel, A. Fung, F. Mohammadi, and K. Raahemifar. 2014. A survey on advanced metering infrastructure. Elsevier Journal of Electrical Power 8 Energy Systems 63 (2014), 473--484.Google ScholarGoogle Scholar
  21. B. Meyer. 1984. Some inequalities for elementary mean values. AMS Mathematics of Computation 42, 165 (1984), 193--194.Google ScholarGoogle ScholarCross RefCross Ref
  22. A. Rad and A.L. Garcia. 2011. Distributed internet-based load altering attacks against smart power grids. IEEE Trans. on Smart Grids 2, 4 (2011), 667--674.Google ScholarGoogle ScholarCross RefCross Ref
  23. E. Shin, A. R. Khamesi, Z. Bahr, S. Silvestri, and D. A. Baker. 2020. A user-centered active learning approach for appliance recognition. IEEE International Conference on Smart Computing (SMARTCOMP), 2020.Google ScholarGoogle ScholarCross RefCross Ref
  24. Y. L. Sun, W. Yu, Z. Han, and K. J. Ray Liu. 2006. Information theoretic framework of trust model and evaluation for ad hoc networks. IEEE Journal on Sel. Areas in Communications 24, 2 (2006), 305--317.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. S. H. Tung. 1975. On lower and upper bounds of the difference between the arithmetic and the geometric mean. AMS Mathematics of Computation 29, 131 (1975), 834--836.Google ScholarGoogle ScholarCross RefCross Ref
  26. J. P. Talusan, F. Tiasus, K. Yasumoto, M. Wilbur, A. Dubey, and S. Bhattacharjee. 2019. Smart transportation delay and resiliency testbed based on information flow of things middleware. IEEE International Conference on Smart Computing (SMARTCOMP), USA, 2019.Google ScholarGoogle Scholar
  27. E. Werley, S. Angelos, O. Saavedra, O. Cortes, and A. Souza. 2011. Detection and identification of abnormalities in customer consumptions in power distribution systems. IEEE Trans. on Power Delivery 26, 4 (2011), 2436--2442.Google ScholarGoogle ScholarCross RefCross Ref
  28. M. Wilbur, A. Dubey, B. Leao, and S. Bhattacharjee. 2020. A decentralized approach for real time anomaly detection in transportation networks. IEEE Conference on Smart Computing, 2020.Google ScholarGoogle Scholar
  29. W. Xia and Y. Chu. 2011. The schur convexity of gini mean values in the sense of harmonic mean. Mathematica Scientia 31, 3 (2011), 1103--1112.Google ScholarGoogle ScholarCross RefCross Ref
  30. W. Yu, D. Griffith, L. Ge, S. Bhattarai, and N. Golmie. 2015. An integrated detection system against false data injection attacks in the Smart Grid. Security and Commun. Networks 8, 2 (2015), 91--109.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. [Online] Available at: https://skyvisionsolutions.files.wordpress.com/2014/08/utility-smart-meters-invade-privacy-22-aug-2014.pdf.Google ScholarGoogle Scholar
  32. NY Times, Last Accessed Oct. 2020, [Online] Available at: http://www.nytimes.com/2009/12/14/us/14meters.html?ref=energy-environment8_r=0.Google ScholarGoogle Scholar
  33. [Online] Last Accessed Oct. 2020, Available at: http://www.telegraph.co.uk/news/2017/03/06/smart-energy-meters-giving-readings-seven-times-high-study-finds/.Google ScholarGoogle Scholar
  34. [Online] Last Accessed Oct. 2020, Available at: https://www.maximintegrated.com/content/dam/files/design/technical-documents/white-papers/smart-grid-security-recent-history-demonstrates.pdf.Google ScholarGoogle Scholar
  35. [Online] Last Accessed Oct. 2020, Available at: https://energy-solution.com/2015/01/29/enabling-automated-demand-response-pge-dras/.Google ScholarGoogle Scholar
  36. [Online] Last Accessed Oct. 2020, Available at: https://www.smartgrid.gov/files/The_Smart_Grid_Promise_DemandSide_Management_201003.pdf.Google ScholarGoogle Scholar
  37. [Online] Last Accessed Oct. 2020, Available at: https://www.smartgrid.gov/project/pecan_street_project_inc_energy_internet_demonstration.html.Google ScholarGoogle Scholar
  38. [Online] Last Accessed Oct. 2020, Available at: http://energy.gov/sites/prod/files/oeprod/DocumentsandMedia/14-AMI_System_Security_Requirements_updated.pdf.Google ScholarGoogle Scholar
  39. [Online] Last Accessed Oct. 2020, Available at: Irish Social Science Data Archives, Available at: http://www.ucd.ie/issda/data/.Google ScholarGoogle Scholar

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