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Securing Metering Infrastructure of Smart Grid: A Machine Learning and Localization Based Key Management Approach

Author

Listed:
  • Imtiaz Parvez

    (Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA)

  • Arif I. Sarwat

    (Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA)

  • Longfei Wei

    (Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA)

  • Aditya Sundararajan

    (Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA)

Abstract

In smart cities, advanced metering infrastructure (AMI) of the smart grid facilitates automated metering, control and monitoring of power distribution by employing a wireless network. Due to this wireless nature of communication, there exist potential threats to the data privacy in AMI. Decoding the energy consumption reading, injecting false data/command signals and jamming the networks are some hazardous measures against this technology. Since a smart meter possesses limited memory and computational capability, AMI demands a light, but robust security scheme. In this paper, we propose a localization-based key management system for meter data encryption. Data are encrypted by the key associated with the coordinate of the meter and a random key index. The encryption keys are managed and distributed by a trusted third party (TTP). Localization of the meter is proposed by a method based on received signal strength (RSS) using the maximum likelihood estimator (MLE). The received packets are decrypted at the control center with the key mapped with the key index and the meter’s coordinates. Additionally, we propose the k-nearest neighbors (kNN) algorithm for node/meter authentication, capitalizing further on data transmission security. Finally, we evaluate the security strength of a data packet numerically for our method.

Suggested Citation

  • Imtiaz Parvez & Arif I. Sarwat & Longfei Wei & Aditya Sundararajan, 2016. "Securing Metering Infrastructure of Smart Grid: A Machine Learning and Localization Based Key Management Approach," Energies, MDPI, vol. 9(9), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:691-:d:76919
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    Citations

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    Cited by:

    1. Ahmed WA Hammad & Ali Akbarnezhad & Assed Haddad & Elaine Garrido Vazquez, 2019. "Sustainable Zoning, Land-Use Allocation and Facility Location Optimisation in Smart Cities," Energies, MDPI, vol. 12(7), pages 1-23, April.
    2. Mohamed S. Abdalzaher & Mostafa M. Fouda & Ahmed Emran & Zubair Md Fadlullah & Mohamed I. Ibrahem, 2023. "A Survey on Key Management and Authentication Approaches in Smart Metering Systems," Energies, MDPI, vol. 16(5), pages 1-27, March.
    3. Mirosław Kornatka & Tomasz Popławski, 2021. "Advanced Metering Infrastructure—Towards a Reliable Network," Energies, MDPI, vol. 14(18), pages 1-12, September.
    4. Sapountzoglou, Nikolaos & Lago, Jesus & De Schutter, Bart & Raison, Bertrand, 2020. "A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids," Applied Energy, Elsevier, vol. 276(C).
    5. Mihai Sanduleac & Gianluca Lipari & Antonello Monti & Artemis Voulkidis & Gianluca Zanetto & Antonello Corsi & Lucian Toma & Giampaolo Fiorentino & Dumitru Federenciuc, 2017. "Next Generation Real-Time Smart Meters for ICT Based Assessment of Grid Data Inconsistencies," Energies, MDPI, vol. 10(7), pages 1-16, June.
    6. Ramesh Ananthavijayan & Prabhakar Karthikeyan Shanmugam & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen & Frede Blaabjerg & Viliam Fedak, 2019. "Software Architectures for Smart Grid System—A Bibliographical Survey," Energies, MDPI, vol. 12(6), pages 1-18, March.
    7. Mohamed S. Abdalzaher & Mostafa M. Fouda & Mohamed I. Ibrahem, 2022. "Data Privacy Preservation and Security in Smart Metering Systems," Energies, MDPI, vol. 15(19), pages 1-19, October.

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