Detecting false data injection attacks in peer to peer energy trading using machine learning

Sara Mohammadi, Frank Eliassen, Yan Zhang, Hans Arno Jacobsen

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

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 paper, 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% detection rate, a 96.18% accuracy rate, and a false negative rate o 1.97% with our approach.

Original languageEnglish
JournalIEEE Transactions on Dependable and Secure Computing
DOIs
StateAccepted/In press - 2021

Keywords

  • Convergence
  • Data models
  • Economics
  • False data injection
  • Games
  • Machine learning
  • Security
  • Support vector machines
  • false data injection attack detection
  • interpretable machine learning
  • local P2P energy trading
  • prosumer
  • smart grid

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