Deep learning-based detection of electricity theft cyber-attacks in smart grid ami networks

Mahmoud Nabil, Muhammad Ismail, Mohamed Mahmoud, Mostafa Shahin, Khalid Qaraqe, Erchin Serpedin

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish
Title of host publicationAdvanced Sciences and Technologies for Security Applications
PublisherSpringer
Pages73-102
Number of pages30
DOIs
Publication statusPublished - 1 Jan 2019

Publication series

NameAdvanced Sciences and Technologies for Security Applications
ISSN (Print)1613-5113
ISSN (Electronic)2363-9466

Fingerprint

Theft
Electricity
larceny
electricity
Learning
infrastructure
Detectors
Advanced metering infrastructures
learning
performance
grid search
false report
customer
energy consumption
environmental pollution
bill
neural network
Communication
monitoring
Deep learning

Keywords

  • AMI networks
  • Cyber-attacks
  • Deep machine learning
  • Electricity theft

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Safety Research
  • Political Science and International Relations
  • Computer Science Applications
  • Computer Networks and Communications
  • Health, Toxicology and Mutagenesis

Cite this

Nabil, M., Ismail, M., Mahmoud, M., Shahin, M., Qaraqe, K., & Serpedin, E. (2019). Deep learning-based detection of electricity theft cyber-attacks in smart grid ami networks. In Advanced Sciences and Technologies for Security Applications (pp. 73-102). (Advanced Sciences and Technologies for Security Applications). Springer. https://doi.org/10.1007/978-3-030-13057-2_4

Deep learning-based detection of electricity theft cyber-attacks in smart grid ami networks. / Nabil, Mahmoud; Ismail, Muhammad; Mahmoud, Mohamed; Shahin, Mostafa; Qaraqe, Khalid; Serpedin, Erchin.

Advanced Sciences and Technologies for Security Applications. Springer, 2019. p. 73-102 (Advanced Sciences and Technologies for Security Applications).

Research output: Chapter in Book/Report/Conference proceedingChapter

Nabil, M, Ismail, M, Mahmoud, M, Shahin, M, Qaraqe, K & Serpedin, E 2019, Deep learning-based detection of electricity theft cyber-attacks in smart grid ami networks. in Advanced Sciences and Technologies for Security Applications. Advanced Sciences and Technologies for Security Applications, Springer, pp. 73-102. https://doi.org/10.1007/978-3-030-13057-2_4
Nabil M, Ismail M, Mahmoud M, Shahin M, Qaraqe K, Serpedin E. Deep learning-based detection of electricity theft cyber-attacks in smart grid ami networks. In Advanced Sciences and Technologies for Security Applications. Springer. 2019. p. 73-102. (Advanced Sciences and Technologies for Security Applications). https://doi.org/10.1007/978-3-030-13057-2_4
Nabil, Mahmoud ; Ismail, Muhammad ; Mahmoud, Mohamed ; Shahin, Mostafa ; Qaraqe, Khalid ; Serpedin, Erchin. / Deep learning-based detection of electricity theft cyber-attacks in smart grid ami networks. Advanced Sciences and Technologies for Security Applications. Springer, 2019. pp. 73-102 (Advanced Sciences and Technologies for Security Applications).
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