SCVIC-CIDS-2022: Bridging Networks and Hosts via Machine Learning-Based Intrusion Detection
- Citation Author(s):
-
Jinxin Liu
(University of Ottawa)
Murat Simsek
(University of Ottawa)
Burak Kantarci
(University of Ottawa)
Mehran Bagheri (Ciena)Petar Djukic
(Ciena)
- Submitted by:
- Burak Kantarci
- Last updated:
- DOI:
- 10.21227/dn9v-3278
- Data Format:
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Abstract
SCVIC-CIDS-2021 was created using the raw data in CIC-IDS-2018*, while this new dataset, SCVIC-CIDS-2022 is formed from NDSec-1** meta-data by following a similar procedure.
This dataset has been used in the following work:
J. Liu, M. Simsek, B. Kantarci, M. Bagheri, P. Djukic, "Bridging Networks and Hosts via Machine Learning-Based Intrusion Detection"; under review in IEEE Transactions on Dependable and Secure Computing.
*Sharafaldin, I.; Habibi Lashkari, A. and Ghorbani, A. (2018). Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. In Proceedings of the 4th International Conference on Information Systems Security and Privacy - ICISSP, ISBN 978-989-758-282-0; ISSN 2184-4356, pages 108-116. DOI: 10.5220/0006639801080116
**Beer, F., Hofer, T., Karimi, D. & Bühler, U., (2017). A new Attack Composition for Network Security. In: Müller, P., Neumair, B., Raiser, H. & Dreo Rodosek, G. (Hrsg.), 10. DFN-Forum Kommunikationstechnologien. Bonn: Gesellschaft für Informatik e.V.. (S. 11-20).
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