Fraud detection in Smart Grids using Signal Temporal Logic

Abstract

Smart grids are defined as complex cyber-physical systems, which combine software controllers and protection systems with electrical power infrastructures. Despite the fact that smart grids boast enhanced resilience, security and reliability in comparison with conventional power infrastructures, they remain susceptible to cyber-attacks. The present paper is concerned with the issue of detecting energy theft, a problem that is addressed through the analysis of energy consumption readings. The objective of this work is to increase the accuracy and performance of current anomaly detectors, offering a comprehensible approach for monitoring the decisions made by real-time classifiers. In this regard, we propose the usage of Signal Temporal Logic (STL), a formalism for expressing properties in real-time signals, as classifiers for detecting anomalies in energy consumption. These STL expressions are instances of Parametric STL formulas, the parameters of which are automatically mined from the energy consumption behaviour of a customer. The paper presents a case study that is evaluated against publicly available real data using existing software tools.