Detecting False Data Injection Attacks in Peer to Peer Energy Trading Using Machine Learning

In peer-to-peer (P2P) energy trading, the incorporation of distributed energy resources with unprotected data, originating from sources such as home energy management systems that are connected through the Internet, provokes vulnerabilities that can manifest security breaches. In this article, two threat scenarios based on a novel false data injection attack (FDIA) model in a local P2P energy trading system are explored. In these scenarios, an attacker gains free energy by manipulating prosumers' consumption and demand. Precise and fast attack detection is needed to guarantee suitable countermeasures to prevent potential risks. We propose a novel instance-based machine learning (ML) classifier for detecting FDIAs. In contrast to black-box ML models, our algorithm provides a transparent decision-making procedure with significant predictive performance. We apply our detection model to a real-world dataset from Austin, Texas. Our experimental results show superior performance as compared to several popular interpretable and non-interpretable ML methods. On average, we achieve a 96.10 percent detection rate, a 96.18 percent accuracy rate, and a false negative rate of 1.97 percent with our approach.