Safety Analysis of AMI Networks Through Smart Fraud Detection

Advanced metering infrastructure (AMI)is a critical part of a modern smart grid that performs the bidirectional data flow of sensitive power information such as smart metering data and control commands. The real-time monitoring and control of the grid are ensured through AMI. While smart meter data helps to improve the overall performance of the grid in terms of efficient energy management, it has also made the AMI an attractive target of cyberattackers with a goal of stealing energy. This is performed through the physical or cyber tampering of the meters, as well as by manipulating the network infrastructure to alter collected data. Proper technology is required for the identification of energy fraud. In this paper, we propose a novel technique to detect fraudulent data from smart meters based on energy consumption patterns of the consumers by utilizing deep learning techniques. We also propose a method for detecting the suspicious relay nodes in the AMI infrastructure that may manipulate the data while forwarding it to the aggregators. We present the performance of our proposed technique, which shows the correctness of the models in identifying the suspicious smart meter data.