Electricity Theft Detection for Smart Homes: Harnessing the Power of Machine Learning With Real and Synthetic Attacks

Abstract

Electricity theft is a pervasive issue with economic implications that necessitate innovative approaches for its detection, given the critical challenge of limited labeled data. However, connecting smart home devices introduces numerous vectors for electricity theft. Therefore, this study introduces an innovative approach to detecting electricity theft in smart homes, leveraging the knowledge-based fine-grained timeseries appliance benign and anomalous consumption patterns. We simulated five attack classes and extended our model's detection capabilities to unknown anomalies across residential settings by segmenting the anonymized data into three different home categories. We validated our experiment using simulated and actual building attack data. The synthetic binary discriminator model (SYNBDM)-Extreme Gradient Boost (XGB), Random Forest, and Multilayer Perceptron (MLP)-outperformed the legacy unsupervised model (LUM)-MLP-Autoencoder(AE), 1D-CONV-AE, and Isolation Forest (RF). XGB had the highest average AUC scores of 98.69% and 98.74% for simulated and attack detection respectively, followed by RF at 96.76% and 97.07%, respectively, across all homes, indicating the robustness of our model in detecting benign and anomalous appliance consumption patterns. This study contributes to the academic discourse in the field and offers practical solutions to energy providers and stakeholders in the smart home industry.