TY - JOUR
T1 - Deep Autoencoder-Based Anomaly Detection of Electricity Theft Cyberattacks in Smart Grids
AU - Takiddin, Abdulrahman
AU - Ismail, Muhammad
AU - Zafar, Usman
AU - Serpedin, Erchin
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - Designing an electricity theft cyberattack detector for the advanced metering infrastructures (AMIs) is challenging due to the limited availability of electricity theft datasets (i.e., malicious datasets). Anomaly detectors, which are trained solely on honest customers' energy consumption profiles (i.e., benign datasets), could potentially overcome this challenge. Unfortunately, existing anomaly detectors in AMIs present shallow architectures and are incapable of capturing the temporal correlations as well as the sophisticated patterns present in the electricity consumption data, which impact their detection performance negatively. This article proposes the adoption of deep (stacked) autoencoders with a long-short-term-memory (LSTM)-based sequence-to-sequence (seq2seq) structure. The depth of the autoencoders' structure helps capture the sophisticated patterns of the data and the seq2seq LSTM model enables exploitation of the time-series nature of data. We investigate the performance of simple autoencoder, variational autoencoder, and autoencoder with attention (AEA), in which improved detection performance is observed when seq2seq structures are adopted compared to fully connected ones.
AB - Designing an electricity theft cyberattack detector for the advanced metering infrastructures (AMIs) is challenging due to the limited availability of electricity theft datasets (i.e., malicious datasets). Anomaly detectors, which are trained solely on honest customers' energy consumption profiles (i.e., benign datasets), could potentially overcome this challenge. Unfortunately, existing anomaly detectors in AMIs present shallow architectures and are incapable of capturing the temporal correlations as well as the sophisticated patterns present in the electricity consumption data, which impact their detection performance negatively. This article proposes the adoption of deep (stacked) autoencoders with a long-short-term-memory (LSTM)-based sequence-to-sequence (seq2seq) structure. The depth of the autoencoders' structure helps capture the sophisticated patterns of the data and the seq2seq LSTM model enables exploitation of the time-series nature of data. We investigate the performance of simple autoencoder, variational autoencoder, and autoencoder with attention (AEA), in which improved detection performance is observed when seq2seq structures are adopted compared to fully connected ones.
KW - Autoencoders
KW - deep machine learning
KW - electricity stealth
KW - hyperparameter optimization
KW - sequence to sequence (seq2seq)
UR - http://www.scopus.com/inward/record.url?scp=85122850248&partnerID=8YFLogxK
U2 - 10.1109/JSYST.2021.3136683
DO - 10.1109/JSYST.2021.3136683
M3 - Article
AN - SCOPUS:85122850248
SN - 1932-8184
VL - 16
SP - 4106
EP - 4117
JO - IEEE Systems Journal
JF - IEEE Systems Journal
IS - 3
ER -