On the Effects of Data Sampling for Deep Learning on Highly Imbalanced Data from SCADA Power Grid Substation Networks for Intrusion Detection

Herbert Mühlburger; Franz Wotawa

On the Effects of Data Sampling for Deep Learning on Highly Imbalanced Data from SCADA Power Grid Substation Networks for Intrusion Detection

Institute of Software Engineering and Artificial Intelligence (7160)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Original language	English
Title of host publication	Proceedings of the 21st IEEE International Conference on Software Quality, Reliability, and Security
Publisher	IEEE Xplore
Publication status	Published - 2021

Fields of Expertise

Information, Communication & Computing

Cite this

On the Effects of Data Sampling for Deep Learning on Highly Imbalanced Data from SCADA Power Grid Substation Networks for Intrusion Detection. / Mühlburger, Herbert ; Wotawa, Franz.
Proceedings of the 21st IEEE International Conference on Software Quality, Reliability, and Security. IEEE Xplore, 2021.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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language = "English",

booktitle = "Proceedings of the 21st IEEE International Conference on Software Quality, Reliability, and Security",

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M3 - Conference paper

BT - Proceedings of the 21st IEEE International Conference on Software Quality, Reliability, and Security

PB - IEEE Xplore

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