Theft Cyberattacks Detection in Smart Grids Based on Machine Learning

Electricity theft is a worldwide issue that adversely impacts companies and users. This issue disrupts the expansion of utility companies, produces electric dangers, and affects the high-level cost of electricity for users. The extensive penetration of advanced metering infrastructure networks gives a chance to identify theft cyberattacks by examining the collected data of the energy consumption from smart meters. This work presents a detection approach based on statistical and machine learning to measure theft confidence. An anomaly detection approach is adopted, in which, to detect suspicious data, a theft detection unit based on a fine tree regression model is constructed. Historical data of average load consumption per unit area, smart meter readings, and temperature are employed in the training stage of the proposed approach. The error between the true and estimated data is fitted by a probability density function to identify suspicious data and determine the theft confidence. Different electricity theft cyberattacks are studied to evaluate the efficacy of the developed approach. The obtained results demonstrate the effectiveness of the developed detection approach.