A Time-Sensitive Model for Data Tampering Detection for the Advanced Metering Infrastructure

• Autorzy: Blanco José Miguel , Rossi Bruno , Pitner Tomas

Identyfikatory

DOI

10.15439/2021F106

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

Smart Grids offer multiple benefits: efficient energy provision, quicker recoveries from failures, etc. Nevertheless, there is risk of data tampering, unsolicited modification of the data of the smart meters. The main aim of this paper is to provide a model for processing the smart meter data that flags any energy consumption level that could be indication of data tampering. The proposed model is time-sensitive, allowing for tracking the energy usage along time, thus making possible the detection of long-lasting abnormal levels of energy consumption. Such model can be integrated in an anomaly detection system and in a semantic web reasoner.

Słowa kluczowe

EN

control engineering; computing; cyber-physical systems; multi-agent systems; software agents

PL

automatyka; informatyka; systemy cyber-fizyczne; systemy wieloagentowe

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 511--519
• Opis fizyczny: Bibliogr. 18 poz., tab., rys.

Twórcy

autor

Blanco José Miguel

• Department of Computer Systems and Communications Faculty of Informatics, Masaryk University Brno, Czech Republic

autor

Rossi Bruno

• Department of Computer Systems and Communications Faculty of Informatics, Masaryk University Brno, Czech Republic

autor

Pitner Tomas

• Department of Computer Systems and Communications Faculty of Informatics, Masaryk University Brno, Czech Republic

Bibliografia

• 1. S. Goel, Y. Hong, V. Papakonstantinou, and D. Kloza, Smart grid security. Springer, 2015.
• 2. X. Fang, S. Misra, G. Xue, and D. Yang, “Smart grid—the new and improved power grid: A survey,” IEEE communications surveys & tutorials, vol. 14, no. 4, pp. 944–980, 2011.
• 3. S. Aoufi, A. Derhab, and M. Guerroumi, “Survey of false data injection in smart power grid: attacks, countermeasures and challenges,” Journal of Information Security and Applications, vol. 54, p. 102518, 2020.
• 4. “Smart - UMass Trace Repository.” [Online]. Available: http://traces.cs.umass.edu/index.php/Smart/Smart
• 5. Z. Erkin, J. R. Troncoso-Pastoriza, R. L. Lagendijk, and F. Pérez-González, “Privacy-preserving data aggregation in smart metering systems: An overview,” IEEE Signal Processing Magazine, vol. 30, no. 2, pp. 75–86, 2013.
• 6. R. Hebner, “Nanogrids, microgrids, and big data: The future of the power grid,” IEEE Spectrum Magazine, p. 23, 2017.
• 7. S. Chren, B. Rossi, and T. Pitner, “Smart grids deployments within eu projects: The role of smart meters,” in 2016 Smart Cities Symposium Prague (SCSP), May 2016. http://dx.doi.org/10.1109/SCSP.2016.7501033. ISSN null pp. 1–5.
• 8. D. B. Avancini, J. J. Rodrigues, S. G. Martins, R. A. Rabêlo, J. Al-Muhtadi, and P. Solic, “Energy meters evolution in smart grids: A review,” Journal of cleaner production, vol. 217, pp. 702–715, 2019.
• 9. S. S. S. R. Depuru, L. Wang, V. Devabhaktuni, and N. Gudi, “Smart meters for power grid—challenges, issues, advantages and status,” in 2011 IEEE/PES Power Systems Conference and Exposition. IEEE, 2011, pp. 1–7.
• 10. G. R. Barai, S. Krishnan, and B. Venkatesh, “Smart metering and functionalities of smart meters in smart grid-a review,” in 2015 IEEE Electrical Power and Energy Conference (EPEC). IEEE, 2015, pp. 138–145.
• 11. S. McLaughlin, B. Holbert, S. Zonouz, and R. Berthier, “Amids: A multi-sensor energy theft detection framework for advanced metering infrastructures,” in 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2012, pp. 354–359.
• 12. S. McLaughlin, B. Holbert, A. Fawaz, R. Berthier, and S. Zonouz, “A multi-sensor energy theft detection framework for advanced metering infrastructures,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 7, pp. 1319–1330, 2013.
• 13. F. Li and B. Luo, “Preserving data integrity for smart grid data aggregation,” in 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm), 2012. http://dx.doi.org/10.1109/Smart-GridComm.2012.6486011 pp. 366–371.
• 14. D. Hock, M. Kappes, and B. Ghita, “Using multiple data sources to detect manipulated electricity meter by an entropy-inspired metric,” Sustainable Energy, Grids and Networks, vol. 21, p. 100290, 2020.
• 15. X. Liu, P. Zhu, Y. Zhang, and K. Chen, “A collaborative intrusion detection mechanism against false data injection attack in advanced metering infrastructure,” IEEE Transactions on Smart Grid, vol. 6, no. 5, pp. 2435–2443, 2015.
• 16. M. Finger and D. M. Gabbay, “Adding a temporal dimension to a logic system,” Journal of Logic, Language and Information, vol. 1, no. 3, pp. 203–233, Sep. 1992 [Online]. Available: https://doi.org/10.1007/BF00156915
• 17. J. Cuenca, F. Larrinaga, and E. Curry, “A Unified Semantic Ontology for Energy Management Applications,” in WSP/WOMoCoE@ISWC, 2017.
• 18. P. Hajder, M. Hajder, M. Liput, and M. Nycz, “Direct communication of edge elements in the industrial internet of things,” in Communication Papers of the 2020 Federated Conference on Computer Science and Information Systems, ser. Annals of Computer Science and Information Systems, S. Agarwal, D. N. Barrell, and V. K. Solanki, Eds., vol. 23. PTI, 2020. http://dx.doi.org/10.15439/2020KM194 pp. 35–42.

Uwagi

1. Track 3: Software, System and Service Engineering

2. Session: Joint 41st IEEE Software Engineering Workshop and 8th International Workshop on Cyber-Physical Systems