SECL: A Zero-Day Attack Detector and Classifier based on Contrastive Learning and Strong Regularization-Reference-Cited by-同舟云学术

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SECL: A Zero-Day Attack Detector and Classifier based on Contrastive Learning and Strong Regularization

Published:2024-07-30 Issue: Volume: Page:1-12
ISSN:
Container-title:Proceedings of the 19th International Conference on Availability, Reliability and Security
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Author:

Duraz Robin¹^ORCID,Espes David²^ORCID,Francq Julien³^ORCID,Vaton Sandrine⁴^ORCID

Affiliation:

1. Chaire of Naval Cyberdefense, Lab-STICC, France

2. UBO, Lab-STICC, France

3. Naval Group (Naval Cyber Laboratory, NCL), France

4. IMT Atlantique, Lab-STICC, France

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3664476.3664505

Reference39 articles.

1. 1999. KDD Cup 99 Data. [Online]. Available:http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.

2. WADI

3. Towards Open World Recognition

4. Gustavo de Carvalho Bertoli, Lourenço Alves Pereira Junior, Aldri Luiz dos Santos, and Osamu Saotome. 2022. Generalizing Intrusion Detection for Heterogeneous Networks: a Stacked-Unsupervised Federated Learning Approach. CoRR (2022). arxiv:2209.00721 [cs.CR] http://arxiv.org/abs/2209.00721v3

5. Learning and the Unknown: Surveying Steps toward Open World Recognition

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