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

Institut für Softwaretechnologie (7160)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Originalsprache	englisch
Titel	Proceedings of the 21st IEEE International Conference on Software Quality, Reliability, and Security
Herausgeber (Verlag)	IEEE Xplore
Publikationsstatus	Veröffentlicht - 2021

Fields of Expertise

Information, Communication & Computing

Dieses zitieren

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.

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

@inproceedings{bf648398bda44bf7ace2d5f68c8f31a0,

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

author = "Herbert M{\"u}hlburger and Franz Wotawa",

year = "2021",

language = "English",

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

publisher = "IEEE Xplore",

}

TY - GEN

T1 - On the Effects of Data Sampling for Deep Learning on Highly Imbalanced Data from SCADA Power Grid Substation Networks for Intrusion Detection

AU - Mühlburger, Herbert

AU - Wotawa, Franz

PY - 2021

Y1 - 2021

M3 - Conference paper

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

PB - IEEE Xplore

ER -