Toward Resilient Smart Grid Communications Using Distributed SDN with ML-Based Anomaly Detection

  • Allen StarkeEmail author
  • Janise McNair
  • Rodrigo Trevizan
  • Arturo Bretas
  • Joshua Peeples
  • Alina Zare
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10866)


Next generation “Smart” systems, including cyber-physical systems like smart grid and Internet-of-Things, integrate control, communication and computation to achieve stability, efficiency and robustness of physical processes. While a great amount of research has gone towards building these systems, security in the form of resilient and fault-tolerant communications for smart grid systems is still immature. In this paper, we propose a hybrid, distributed and decentralized (HDD) SDN architecture for resilient Smart Systems. It provides a redundant controller design for fault-tolerance and fail-over operation, as well as parallel execution of multiple anomaly detection algorithms. Using the k-means clustering algorithm from the machine learning literature, it is shown that k-means can be used to produce a high accuracy (96.9%) of identifying anomalies within normal traffic. Furthermore, incremental k-means produces a slightly lower accuracy (95.6%) but demonstrated an increased speed with respect to k-means and fewer CPU and memory resources needed, indicating a possibility for scaling the system to much larger networks.


Software defined networks Anomaly detection Machine learning Security Resilience 



The authors would like to thank the Harris Corporation Excellence in Research program for providing funding for this research.


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Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Allen Starke
    • 1
    Email author
  • Janise McNair
    • 1
  • Rodrigo Trevizan
    • 1
  • Arturo Bretas
    • 1
  • Joshua Peeples
    • 1
  • Alina Zare
    • 1
  1. 1.University of FloridaGainesvilleUSA

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