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How Blockchain Could Improve Fraud Detection in Power Distribution Grid

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 771))

Abstract

Power utilities experience large losses of electricity in distribution from power plants to the end consumer. There are two types of losses: technical and non-technical. Among non-technical losses is a very prominent one: electrical fraud. In this paper we propose a new system to detect fraud. A blockchain is used to store the data collected by the WSN that monitors the power distribution grid. Using data stored in the blockchain, it is constructed directed directed acyclic graph (DAG) with non-technical losses and applied the clustering algorithm created to detect fraud. The main advantage of blockchain to our model is that every time the blockchain grows the stored data is more secure. Therefore, power utilities can perform an inspection in blockchain data stored.

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References

  1. Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 94–105 (1998)

    Google Scholar 

  2. Announcing the Secure Hash Standard, 1 August 2002. http://csrc.nist.gov/publications/fips/fips180-2/fips180-2.pdf

  3. Antonopoulos, A.M.: Mastering Bitcoin: Unlocking Digital Cryptocurrencies, 1st edn. O’Reilly Media Inc., Sebastopol (2014)

    Google Scholar 

  4. Biswas, S., Das, R., Chatterjee, P.: Energy-efficient connected target coverage in multi-hop wireless sensor networks. In: Industry Interactive Innovations in Science, Engineering and Technology, pp. 411–421. Springer, Singapore (2018)

    Google Scholar 

  5. Chamoso, P., Rivas, A., Martín-Limorti, J.J., Rodríguez, S.: A hash based image matching algorithm for social networks. In: De la Prieta, F., Vale, Z., Antunes, L., Pinto, T., Campbell, A.T., Julián, V., Neves, A.J.R., Moreno, M.N. (eds.) PAAMS 2017. AISC, vol. 619, pp. 183–190. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61578-3_18

    Google Scholar 

  6. Cody, C., Ford, V., Siraj, A.: Decision tree learning for fraud detection in consumer energy consumption. In: Proceedings of the IEEE 14th International Conference on Machine Learning and Applications, pp. 1175–1179, December 2015

    Google Scholar 

  7. Costa, Â., Novais, P., Corchado, J.M., Neves, J.: Increased performance and better patient attendance in an hospital with the use of smart agendas. Log. J. IGPL 20(4), 689–698 (2012)

    Article  MathSciNet  Google Scholar 

  8. Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yáñez, J.C.: Neuro-symbolic System for Business Internal Control. In: Industrial Conference on Data Mining, pp. 1–10 (2004)

    Google Scholar 

  9. Tapia, D.I., Alonso, R.S., García, O., Corchado, J.M., Bajo, J.: Wireless sensor networks, real-time locating systems and multi-agent systems: the perfect team. In: 2013 16th International Conference on Information Fusion (FUSION), pp. 2177–2184 (2013)

    Google Scholar 

  10. Depuru, S.S.S.R., Wang, L., Devabhaktuni, V.: Support vector machine based data classification for detection of electricity theft. In: Power Systems Conference and Exposition (PSCE) 2011 IEEE/PES, pp. 1–8, 20–23 March 2011

    Google Scholar 

  11. Double-Spending—Bitcoin WiKi. https://en.bitcoin.it/wiki/Double-spending. Accessed 15 Mar 2016

  12. Dwyer, G.: The economics of Bitcoin and similar private digital currencies. J. Financ. Stab. 17, 81–91 (2015)

    Article  Google Scholar 

  13. Eris Industries Documentation—Blockchains. https://docs.erisindustries.com/explainers/blockchains/. Accessed 15 Mar 2016

  14. Fan, H., Mei, X., Prokhorov, D.V., Ling, H.: Multi-level Contextual RNNs with Attention Model for Scene Labeling. CoRR (2016). abs/1607.02537

    Google Scholar 

  15. Farooq, M.O., Kunz, T.: Operating systems for wireless sensor networks: a survey. Sensors 11, 5900–5930 (2011)

    Article  Google Scholar 

  16. Ford, V., Siraj, A., Eberle, W.: Smart grid energy fraud detection using artificial neural networks. In: IEEE Symposium on Computational Intelligence Applications in Smart Grid 2014, pp. 1–6, 9–12 December 2014

    Google Scholar 

  17. García-Valls, M.: Prototyping low-cost and flexible vehicle diagnostic systems. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. Salamanca 5(4), 93–103 (2016)

    Article  Google Scholar 

  18. Coria, J.A.G., Castellanos-Garzón, J.A., Corchado, J.M.: Intelligent business processes composition based on multi-agent systems. Expert Syst. Appl. 41(4), 1189–1205 (2014). Part 1

    Article  Google Scholar 

  19. Greenspan, G.: Avoiding the Pointless Blockchain Project (2015). http://www.multichain.com/blog/2015/11/avoidingpointless-blockchain-project/

  20. Hashcash-Bitcoin WiKi. https://en.bitcoin.it/wiki/Hashcash. Accessed 15 Mar 2016

  21. Huang, C.F., Tseng, Y.C.: A survey of solutions to the coverage problems in wireless sensor networks. J. Int. Technol. 6, 1–8 (2005)

    Google Scholar 

  22. Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: Random finite set-based bayesian filters using magnitude-adaptive target birth intensity. In: FUSION 2014–17th International Conference on Information Fusion (2014). https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910637788&partnerID=40&md5=bd8602d6146b014266cf07dc35a681e0

  23. Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: A particle dyeing approach for track continuity for the SMC-PHD filter. In: FUSION 2014–17th International Conference on Information Fusion (2014). https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910637583&partnerID=40&md5=709eb4815eaf544ce01a2c21aa749d8f

  24. Li, T., Sun, S., Bolić, M., Corchado, J.M.: Algorithm design for parallel implementation of the SMC-PHD filter. Sig. Process. 119, 115–127 (2016). https://doi.org/10.1016/j.sigpro.2015.07.013

    Article  Google Scholar 

  25. Lima, A.C.E.S., De Castro, L.N., Corchado, J.M.: A polarity analysis framework for Twitter messages. Appl. Math. Comput. 270, 756–767 (2015). https://doi.org/10.1016/j.amc.2015.08.059

    Article  Google Scholar 

  26. McLaughlin, S., Podkuiko, D., McDaniel, P.: Energy theft in the advanced metering infrastructure. In: Critical Information Infrastructures, Security, pp. 176–187 (2010)

    Chapter  Google Scholar 

  27. Mettler, M.: Blockchain technology in healthcare: the revolution starts here. In: 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), Munich, pp. 1–3 (2016)

    Google Scholar 

  28. Moinet, A., Benoît, D., Jean-Luc, B.: Blockchain based trust & authentication for decentralized sensor networks. arXiv preprint arXiv:1706.01730 (2017)

  29. Monedero, Í, Biscarri, F., León, C., Biscarri, J., Millán, R.: MIDAS: detection of non-technical losses in electrical consumption using neural networks and statistical techniques. In: Gavrilova, M.L., Gervasi, O., Kumar, V., Tan, C.J.K., Taniar, D., Laganá, A., Mun, Y., Choo, H. (eds.) ICCSA 2006. LNCS, vol. 3984, pp. 725–734. Springer, Heidelberg (2006). https://doi.org/10.1007/11751649_80

    Chapter  Google Scholar 

  30. Nakamoto, S.: Bitcoin: A Peer-to-Peer Electronic Cash System (2008). https://bitcoin.org/bitcoin.pdf

  31. Oprescu, F.: Method and apparatus for unique address assignment, node self-identification and topology mapping for a directed acyclic graph. U.S. Patent No. 5,394,556, 28, February 1995

    Google Scholar 

  32. Pinto, A., Costa, R.: Hash-chain-based authentication for IoT. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. J. Salamanca 5(4) 43–57 (2016)

    Article  Google Scholar 

  33. Powell, A.P., Alex, M.W.: Constraint evaluation in directed acyclic graphs. U.S. Patent No. 9,892,529. 13, February 2018

    Google Scholar 

  34. Prieto Tejedor, J., Chamoso Santos, P., de la Prieta Pintado, F., Corchado Rodríguez, J.M.: A generalized framework for wireless localization in gerontechnology. In: 17th IEEE International Conference on Ubiquitous Wireless Broadband ICUWB’2017. IEEE, September 2017

    Google Scholar 

  35. Kelly, J., Williams, A.: Forty Big Banks Test Blockchain-Based Bond Trading System (2016). http://www.nytimes.com/reuters/2016/03/02/business/02reuters-bankingblockchain-bonds.html

  36. Kupriyanovsky, Y., et al.: Smart container, smart port, BIM, Internet Things and blockchain in the digital system of world trade. Int. J. Open Inf. Technol. 6(3), 49–94 (2018)

    Google Scholar 

  37. Kambourakis, G., Gomez Marmol, F., Wang, G.: Security and Privacy in Wireless and Mobile Networks, 18 (2018)

    Article  Google Scholar 

  38. Rodríguez, S., De Paz, J.F., Villarrubia, G., Zato, C., Bajo, J.: Corchado, multi-agent information fusion system to manage data from a WSN in a residential home. Inf. Fusion 23, 43–57 (2015)

    Article  Google Scholar 

  39. Rodríguez, S., Zato, C., Corchado, J.M., Li, T.: Fusion system based on multi-agent systems to merge data from WSN. In: 2014 17th International Conference on Information Fusion (FUSION), pp. 1–8 (2014)

    Google Scholar 

  40. Satoshi, N.: Bitcoin: A Peer-to-Peer Electronic Cash System (2008). https://bitcoin.org/bitcoin.pdf

  41. Sobral, J.V.V., Rodrigues, J.J.P.C., Saleem, K., de Paz, J.F., Corchado, J.M.: A composite routing metric for wireless sensor networks in AAL-IoT. In: Wireless and Mobile Networking Conference (WMNC), 2016 9th IFIP, pp. 168–173 (2016)

    Google Scholar 

  42. Spiric, J.V., Doi, M.B., Stankovi, S.S.: Fraud detection in registered electricity time series. Int. J. Electr. Power Energy Syst. 71, 42–50 (2015)

    Article  Google Scholar 

  43. Tapia, D.I., Fraile, J.A., Rodríguez, S., Alonso, R.S., Corchado, J.M.: Integrating hardware agents into an enhanced multi-agent architecture for ambient intelligence systems. Inf. Sci. 222, 47–65 (2013)

    Article  Google Scholar 

  44. Tang, Y., Ten, C.W., Brown, L.E.: Switching reconfiguration of fraud detection within an electrical distribution network. In: 2017 Resilience Week (RWS). Wilmington, DE, pp. 206–212 (2017)

    Google Scholar 

  45. Casado-Vara, R., Corchado, J.M., Blockchain for democratic voting: how blockchain could cast off voter fraud. Orient. J. Comp. Sci. Technol. 11(1). http://www.computerscijournal.org/?p=8042

    Article  Google Scholar 

  46. Redondo-Gonzalez, E., De Castro, L.N., Moreno-Sierra, J., Maestro De Las Casas, M.L., Vera-Gonzalez, V., Ferrari, D.G., Corchado, J.M.: Bladder carcinoma data with clinical risk factors and molecular markers: a cluster analysis. BioMed Res. Int. 2015, 14 (2015)

    Google Scholar 

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Acknowledgments

This paper has been funded by the European Regional Development Fund (FEDER) within the framework of the Interreg program V-A Spain-Portugal 2014-2020 (PocTep) grant agreement No 0123_IOTEC_3_E (project IOTEC).

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Correspondence to Roberto Casado-Vara .

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Casado-Vara, R., Prieto, J., Corchado, J.M. (2019). How Blockchain Could Improve Fraud Detection in Power Distribution Grid. In: Graña, M., et al. International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-319-94120-2_7

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