Deep recurrent electricity theft detection in ami networks with evolutionary hyper-parameter tuning

Mahmoud Nabil, Mohamed Mahmoud, Muhammad Ismail, Erchin Serpedin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Despite its prominent role in future smart power grid for monitoring and billing purposes, advanced metering infrastructure (AMI) opens the door wide for serious cyber-security threats. This paper investigates an electricity theft cyber-Attack scenario where malicious customers hack their smart meters and manipulate the energy consumption readings in order to reduce their electricity bills. In this context, machine learning techniques offer an attractive means to efficiently detect such cyber-Attacks. The contributions of this work are two-fold: 1) Unlike the existing works that rely on shallow and static machine learning techniques, we propose a detection mechanism based on deep recurrent neural networks that exploit the time-series nature of the energy consumption readings to improve the detection performance, and 2) While the existing works did not consider fine-Tuning of the detector's hyper-parameters, we employ a multi-objective evolutionary (Non-dominated Sorting Genetic Algorithm)-based optimization approach to fine-Tune the detector's hyper-parameters to further improve the detection performance. Real energy consumption data is utilized to test the proposed detection mechanism, which demonstrates a superior detection performance compared with a state-of-The-Art (static and shallow) detector.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Congress on Cybermatics
Subtitle of host publication12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1002-1008
Number of pages7
ISBN (Electronic)9781728129808
DOIs
Publication statusPublished - Jul 2019
Event12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019 - Atlanta, United States
Duration: 14 Jul 201917 Jul 2019

Publication series

NameProceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019

Conference

Conference12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019
CountryUnited States
CityAtlanta
Period14/7/1917/7/19

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Keywords

  • AMI networks
  • Cyber-Attacks
  • Deep machine learning
  • electricity theft

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Renewable Energy, Sustainability and the Environment
  • Hardware and Architecture
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Communication

Cite this

Nabil, M., Mahmoud, M., Ismail, M., & Serpedin, E. (2019). Deep recurrent electricity theft detection in ami networks with evolutionary hyper-parameter tuning. In Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019 (pp. 1002-1008). [8875329] (Proceedings - 2019 IEEE International Congress on Cybermatics: 12th IEEE International Conference on Internet of Things, 15th IEEE International Conference on Green Computing and Communications, 12th IEEE International Conference on Cyber, Physical and Social Computing and 5th IEEE International Conference on Smart Data, iThings/GreenCom/CPSCom/SmartData 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00175