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:
Wed, 09/14/2022 - 14:30
DOI:
10.21227/dn9v-3278
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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).

 

Instructions: 

Please see the attached file for instructions.

Funding Agency: 
Ontario Center for Innovation (OCI)
Grant Number: 
5G ENCQOR 31993