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Prediction-Based Data Augmentation for Smart Grid Line Outage Detection

https://doi.org/10.1109/NAPS61145.2024.10741749

Rogers, Jennifer; Lin, Hui; Sun, Yan Lindsay (October 2024, IEEE)

Free, publicly-accessible full text available October 13, 2025
Learning from Future: Prediction-based Data Augmentation to Enhance Power Grids Fault Detection

https://doi.org/10.1109/SEGE59172.2023.10274545

Rogers, Jennifer; Danilczyk, William; Lin, Hui; Sun, Yan Lindsay (August 2023, IEEE)

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A Real Time Physics-Based Industrial Control System Honeynet Architecture for the Smart Grid

https://doi.org/10.1109/ICINT58947.2023.00014

De_Pace, Gabriel; Lin, Hui; Sun, Yan Lindsay (May 2023, IEEE)

Full Text Available
Leibniz International Proceedings in Informatics (LIPIcs):28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

https://doi.org/10.4230/LIPIcs.CP.2022.17

Curry, Timothy; De Pace, Gabriel; Fuller, Benjamin; Michel, Laurent; Sun, Yan (January 2022, Constraint Programming)
Solnon, Christine (Ed.)
Software defined networks (SDNs) define a programmable network fabric that can be reconfigured to respect global networks properties. Securing against adversaries who try to exploit the network is an objective that conflicts with providing functionality. This paper proposes a two-stage mixed-integer programming framework. The first stage automates routing decisions for the flows to be carried by the network while maximizing readability and ease of use for network engineers. The second stage is meant to quickly respond to security breaches to automatically decide on network counter-measures to block the detected adversary. Both stages are computationally challenging and the security stage leverages large neighborhood search to quickly deliver effective response strategies. The approach is evaluated on synthetic networks of various sizes and shown to be effective for both its functional and security objectives.
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