Abstract
The periodic transmission of the customers' power consumption readings in the advanced metering infrastructure (AMI) is essential for energy management and billing. To collect the readings efficiently, the change and transmit approach is adopted in AMI (CAT AMI) so that the readings are reported only when there is enough change in the consumption. However, CAT AMI suffers from malicious customers who launch electricity-theft cyberattacks by manipulating their readings to illegally reduce their bills. These attacks can cause hefty financial losses and degrade the grid performance because the readings are used for grid management. In this article, the electricity-theft problem in CAT AMI networks is investigated. We first process a real power consumption readings data set to create a benign data set and propose a new set of cyberattacks to create malicious samples. We then develop a deep-learning-based electricity-theft detection solution to identify malicious customers for the CAT AMI network. The proposed detector uses both the customers' transmission pattern and CAT readings to learn the correlation between them in order to enhance the detector's ability in identifying electricity thefts. We conduct extensive experiments to evaluate the performance of our electricity-theft detector, and the results indicate that our detector can accurately detect malicious customers and achieve higher detection rate and lower false alarm than the detectors that are trained only on the CAT readings.