Detecting Electricity Theft Cyber-attacks in CAT AMI System Using Machine Learning

Abstract

There are two power consumption readings collection approaches adopted in the advanced metering infrastructure (AMI) of the smart grid; periodic transmission (PT) and change and transmit (CAT) AMI systems. Among these approaches, CAT is a promising approach that collects these readings efficiently by sending the readings only when there is enough change in consumption to reduce the number of transmitted readings. However, CAT AMI system suffers from electricity theft cyber-attacks that can be launched by malicious customers who may compromise their meters and manipulate their power consumption readings to illegally reduce their bills. These attacks do not only cause hefty financial losses but may also degrade the grid performance because the readings are used for grid management. Therefore, this paper is the first work that investigates this problem for CAT AMI system, in which the power consumption readings are not sent periodically to the system operator. We first prepare a benign dataset for the CAT AMI by processing a real power consumption readings dataset. Next, we propose a new set of attacks tailored for the CAT AMI to create a malicious dataset. Then, we propose a general and hybrid deep-learning electricity theft detector to identify malicious customers. The proposed detector is trained on both benign and malicious data from all customers using the reported CAT readings. Extensive test studies are carried out to investigate the detector’s performance using publicly available real data of power consumption from 114 customers. Simulation results demonstrate our models can detect malicious customers with high detection rate and low false alarm.

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