Detecting Electricity Fraud in the Net-Metering System Using Deep Learning
Mahmoud M. Badr, Mohamed I. Ibrahem, Mohamed Baza, Mohamed Mahmoud, Waleed Alasmary
Abstract
There are different metering systems adopted in the advanced metering infrastructure (AMI) of the smart grid. Among these systems, the net-metering is a promising system that motivates customers to install renewable resources at their premises to generate electricity and sell it to the utility. In this system, the customer’s home is equipped with one smart meter to report the net readings representing the difference between the power consumed from the power grid and the power injected into the grid. However, malicious customers may compromise their meters to report false readings to the utility to illegally achieve financial gains. This not only causes huge losses to the utility, but also deteriorates the grid performance. To the best of our knowledge, this problem has not been investigated. Therefore, in this paper, we investigate the detection of false-reading attacks in the net-metering system for the first time. Specifically, we propose four sophisticated attacks customized for the net-metering system and use them to create a dataset containing both benign and malicious samples. We have analyzed the dataset and detected time correlations between the readings within the benign samples. Based on the data analysis, we propose a general deep-learning-based detector with hybrid architecture involving convolutional neural network (CNN) and gated recurrent unit neural network (GRU). We have evaluated our detector, and the results demonstrate that the detector can detect the false-reading attacks with high precision and recall, and low false alarm.