The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/6b865a28-683d-4ace-a537-dbaa8cf9ee63

Presekal, Alfan (2025): Data underlying the PhD dissertation: Advanced Persistent Threat Detection and Correlation for Cyber-Physical Power Systems. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/6b865a28-683d-4ace-a537-dbaa8cf9ee63.v1

Categories

• Artificial Intelligence and Image Processing
• Electrical and Electronic Engineering
• Engineering
• Information and Computing Sciences

Keywords

Advanced Persistent Threats Artificial Intelligence Cyber Security Cyber-Physical Power System Operational Technology Smart Grids Spatio-Temporal Correlation

Licence

CC BY 4.0

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by Alfan Presekal

This dataset contains the data related to the dissertation on advanced persistent threat detection and correlation for cyber-physical power systems. The data includes input data, processing script, output, and figures related to the dissertation.

History

• 2025-01-10 first online, published, posted

Publisher

4TU.ResearchData

Format

spreadsheet/.csv script/.py image/.png image/.pdf

Associated peer-reviewed publication

Attack Graph Model for Cyber-Physical Power Systems Using Hybrid Deep Learning

References

• https://doi.org/10.1109/TSG.2023.3237011
• https://doi.org/10.1109/TSG.2024.3474039
• https://doi.org/10.1109/ACCESS.2024.3507386

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics, and Computer Science, Department of Electrical Sustainable Energy