The doi 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

Dataset

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

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

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

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.

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

4TU.ResearchData

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

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

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

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