Deep Autoencoder-based Detection of Electricity Stealth Cyberattacks in AMI Networks

Efficient recognition of electricity stealth cyberattacks within the advanced metering infrastructures (AMIs) is limited by the lack of deliberate electricity theft datasets. Thus, anomaly-based detectors trained using only the information provided by the electricity usage profiles of truthful customers can overcome this limitation. This paper investigates the performance of deep (stacked) basic autoencoders (BAEs) with fully connected (FC) and long-short-term-memory (LSTM) structures for electricity thefts identification. The performance of the BAEs is compared to existing anomaly detectors such as one-class support vector machine (SVM) and auto-regressive integrated moving average (ARIMA) models as benchmarks. This study shows an enhancement of 8-9% in true positive rate and 7-16% in false positive rate for the deep LSTM-based BAE architecture relative to the benchmark anomaly detectors.