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Deep Learning-Based Detection of Electricity Theft Cyber-Attacks in Smart Grid AMI Networks

  • Mahmoud NabilEmail author
  • Muhammad Ismail
  • Mohamed Mahmoud
  • Mostafa Shahin
  • Khalid Qaraqe
  • Erchin Serpedin
Chapter
Part of the Advanced Sciences and Technologies for Security Applications book series (ASTSA)

Abstract

Advanced metering infrastructure (AMI) is the primary step to establish a modern smart grid. AMI enables a flexible two-way communication between smart meters and utility company for monitoring and billing purposes. However, AMI suffers from the deceptive behavior of malicious consumers who report false electricity usage in order to reduce their bills, which is known as electricity theft cyber-attacks. In this chapter, we present deep learning-based detectors that can efficiently thwart electricity theft cyber-attacks in smart grid AMI networks. First, we present a customer-specific detector based on a deep feed-forward and recurrent neural networks (RNN). Then, we develop generalized electricity theft detectors that are more robust against contamination attacks compared with customer-specific detectors. In all detectors, optimization of hyperparameters is investigated to improve the performance of the developed detectors. In particular, the hyperparameters of the detectors are optimized via sequential, random, and genetic optimization-based grid search approaches. Extensive test studies are carried out against real energy consumption data to investigate all detectors performance. Also, the performance of the developed deep learning-based detectors is compared with a shallow machine learning approach and a superior performance is observed for the deep learning-based detectors.

Keywords

Electricity theft Cyber-attacks AMI networks Deep machine learning 

Notes

Acknowledgements

This publication was made possible by NPRP grant # NPRP9-055-2-022 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mahmoud Nabil
    • 1
    Email author
  • Muhammad Ismail
    • 2
  • Mohamed Mahmoud
    • 1
  • Mostafa Shahin
    • 3
  • Khalid Qaraqe
    • 2
  • Erchin Serpedin
    • 2
  1. 1.Department of Electrical and Computer EngineeringTennessee Technical UniversityCookevilleUSA
  2. 2.Electrical and Computer Engineering DepartmentTexas A&M University at QatarDohaQatar
  3. 3.School of Electrical Engineering and TelecommunicationsUniversity of New South WalesSydneyAustralia

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