Transferability of machine learning models learned from public intrusion detection datasets: the CICIDS2017 case study-Reference-Cited by-同舟云学术

Transferability of machine learning models learned from public intrusion detection datasets: the CICIDS2017 case study

Published:2022-03-19 Issue:4 Volume:30 Page:955-981
ISSN:0963-9314
Container-title:Software Quality Journal
language:en
Short-container-title:Software Qual J

Author:

Catillo Marta^ORCID,Del Vecchio Andrea,Pecchia Antonio,Villano Umberto

Publisher

Springer Science and Business Media LLC

Subject

Safety, Risk, Reliability and Quality,Software

Link

https://link.springer.com/content/pdf/10.1007/s11219-022-09587-0.pdf

Reference36 articles.

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2. Ahmim, A., Maglaras, L., Ferrag, M. A., Derdour, M., & Janicke, H. (2019). A novel hierarchical intrusion detection system based on decision tree and rules-based models. In Proc. International Conference on Distributed Computing in Sensor Systems (pp. 228–233). IEEE.

3. Ali, O., & Cotae, P. (2018). Towards DoS/DDoS attack detection using artificial neural networks. In Proc. Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (pp. 229–234). IEEE.

4. Beer, F., Hofer, T., Karimi, D., & Bühler, U. (2017). A new attack composition for network security. In 10. DFN-Forum Kommunikationstechnologien (pp. 11–20). Gesellschaft fur Informatik e.V.

5. Bowen, T., Poylisher, A., Serban, C., Chadha, R., Jason Chiang, C., & Marvel, L. M. (2016). Enabling reproducible cyber research - Four labeled datasets. In Proc. Military Communications Conference (pp. 539–544). IEEE.

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