@inproceedings{2171800f1845485cae38019c61d90a69,
title = "Efficient detection of electricity theft cyber attacks in AMI networks",
abstract = "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.",
keywords = "AMI networks, Cyber attacks, Deep machine learning, Electricity theft detection",
author = "Muhammad Ismail and Mostafa Shahin and Shaaban, {Mostafa F.} and Erchin Serpedin and Khalid Qaraqe",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Wireless Communications and Networking Conference, WCNC 2018 ; Conference date: 15-04-2018 Through 18-04-2018",
year = "2018",
month = jun,
day = "8",
doi = "10.1109/WCNC.2018.8377010",
language = "English",
series = "IEEE Wireless Communications and Networking Conference, WCNC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
booktitle = "2018 IEEE Wireless Communications and Networking Conference, WCNC 2018",
address = "United States",
}