Efficient detection of electricity theft cyber attacks in AMI networks Conference Paper uri icon

abstract

  • © 2018 IEEE. Advanced metering infrastructure (AMI) networks are vulnerable against electricity theft cyber attacks. Different from the existing research that exploits shallow machine learning architectures for electricity theft detection, this paper proposes a deep neural network (DNN)-based customer-specific detector that can efficiently thwart such cyber attacks. The proposed DNN-based detector implements a sequential grid search analysis in its learning stage to appropriately fine tune its hyper-parameters, hence, improving the detection performance. Extensive test studies are carried out based on publicly available real energy consumption data of 5000 customers and the detector's performance is investigated against a mixture of different types of electricity theft cyber attacks. Simulation results demonstrate a significant performance improvement compared with state-of-the-art shallow detectors.

name of conference

  • 2018 IEEE Wireless Communications and Networking Conference (WCNC)

published proceedings

  • 2018 IEEE Wireless Communications and Networking Conference (WCNC)

author list (cited authors)

  • Ismail, M., Shahin, M., Shaaban, M. F., Serpedin, E., & Qaraqe, K.

citation count

  • 17

complete list of authors

  • Ismail, Muhammad||Shahin, Mostafa||Shaaban, Mostafa F||Serpedin, Erchin||Qaraqe, Khalid

publication date

  • April 2018

publisher