The 1st EAI International Conference on Smart Grid Assisted Internet of Things

Research Article

Diagnosing False Data Injection Attacks in the Smart Grid: a Practical Framework for Home-area Networks

Download64 downloads
  • @INPROCEEDINGS{10.4108/eai.7-8-2017.152988,
        author={Adrian Padin and Yeabsera Kebede and Maxwell Morgan and Davis Vorva and Atman Fozdar and Richard Kalvaitis and Nikolas Remley and Samir Tout and Michael G. Kallitsis},
        title={Diagnosing False Data Injection Attacks in the Smart Grid: a Practical Framework for Home-area Networks},
        proceedings={The 1st EAI International Conference on Smart Grid Assisted Internet of Things},
        publisher={EAI},
        proceedings_a={SGIOT},
        year={2017},
        month={8},
        keywords={Smart grid anomaly detection false data injection attacks statistics algorithms software monitoring real-world measurements.},
        doi={10.4108/eai.7-8-2017.152988}
    }
    
  • Adrian Padin
    Yeabsera Kebede
    Maxwell Morgan
    Davis Vorva
    Atman Fozdar
    Richard Kalvaitis
    Nikolas Remley
    Samir Tout
    Michael G. Kallitsis
    Year: 2017
    Diagnosing False Data Injection Attacks in the Smart Grid: a Practical Framework for Home-area Networks
    SGIOT
    EAI
    DOI: 10.4108/eai.7-8-2017.152988
Adrian Padin1, Yeabsera Kebede1, Maxwell Morgan2, Davis Vorva2, Atman Fozdar3, Richard Kalvaitis3, Nikolas Remley3, Samir Tout3, Michael G. Kallitsis4,*
  • 1: Computer Science and Engineering, University of Michigan, Ann Arbor, USA, Merit Network, Inc., University of Michigan, Ann Arbor, USA
  • 2: Computer Science and Engineering, University of Michigan, Ann Arbor, USA,
  • 3: Information Assurance, Eastern Michigan University, Ypsilanti, USA,
  • 4: Merit Network, Inc., University of Michigan, Ann Arbor, USA
*Contact email: mgkallit@merit.edu

Abstract

Advances in the metering infrastructure of the electric grid allow two-way communication capabilities between the utility center and a vast array of smart meters installed in the grid's distribution and transmission components. Nefarious users that manage to compromise insecure smart meters can alter the payload transmitted from these meters, and abruptly increase or reduce electricity demand in a coordinated manner. This malicious practice, known as false data injection attack, can destabilize the grid. This paper describes a practical framework for diagnosing false data injection attacks in the smart grid. We propose a behavioral-based monitoring system that can be installed at home-area networks for detecting the aforementioned anomalies. We demonstrate a real-world prototype of our system engineered with inexpensive devices such as Raspberry Pi's and Z-Wave wireless sensors, and evaluate its performance with real data.