Learning From Few Cyber-Attacks: Addressing the Class Imbalance Problem in Machine Learning-Based Intrusion Detection in Software-Defined Networking
- DOI
- 10.1109/access.2023.3341755
- Published
- 2023
- Container
- IEEE Access
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/access.2023.3341755,
title = {Learning From Few Cyber-Attacks: Addressing the Class Imbalance Problem in Machine Learning-Based Intrusion Detection in Software-Defined Networking},
author = {Seyed Mohammad Hadi Mirsadeghi and Hayretdin Bahsi and Risto Vaarandi and Wissem Inoubli},
year = {2023},
journal = {IEEE Access},
doi = {10.1109/access.2023.3341755},
url = {https://doi.org/10.1109/access.2023.3341755}
}RIS
TY - JOUR TI - Learning From Few Cyber-Attacks: Addressing the Class Imbalance Problem in Machine Learning-Based Intrusion Detection in Software-Defined Networking AU - Seyed Mohammad Hadi Mirsadeghi AU - Hayretdin Bahsi AU - Risto Vaarandi AU - Wissem Inoubli PY - 2023 JO - IEEE Access DO - 10.1109/access.2023.3341755 UR - https://doi.org/10.1109/access.2023.3341755 ER -
APA
Mirsadeghi, S. M. H., Bahsi, H., Vaarandi, R., & Inoubli, W. (2023). Learning From Few Cyber-Attacks: Addressing the Class Imbalance Problem in Machine Learning-Based Intrusion Detection in Software-Defined Networking. IEEE Access. https://doi.org/10.1109/access.2023.3341755
Source records
- crossref · retrieved 2026-09-25T14:10:46.734Z