Smart Fraud Detection in Smart Metering System of AMI Networks

Abstract

Advanced metering infrastructure (AMI) is a critical part of modern smart grids. It performs the delivery of sensitive power information such as smart metering data of power consumption. 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. We propose a Machine Learning based technique to detect fraudulent data from smart meters based on energy consumption patterns of the consumers by utilizing both supervised and unsupervised techniques. We analyze the performance of our proposed technique and show the correctness of the models in identifying the suspicious smart meter data.