Increasing Detection Rate for Imbalanced Malicious Traffic using Generative Adversarial Networks-Reference-Cited by-同舟云学术

Increasing Detection Rate for Imbalanced Malicious Traffic using Generative Adversarial Networks

Published:2024-06-05 Issue: Volume:456 Page:74-81
ISSN:
Container-title:European Interdisciplinary Cybersecurity Conference
language:
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Author:

Memmesheimer Pascal¹^ORCID,Machmeier Stefan²^ORCID,Heuveline Vincent²^ORCID

Affiliation:

1. Engineering Mathematics and Computing Lab, Interdisciplinary Center for Scientific Computing of Heidelberg University, Germany and Data Analysis and Modeling in Medicine, Medical Faculty Mannheim of Heidelberg University, Germany and Data Mining and Uncertainty Quantification, Heidelberg Institute for Theoretical Studies, Germany

2. Engineering Mathematics and Computing Lab, Interdisciplinary Center for Scientific Computing of Heidelberg University, Germany and Data Mining and Uncertainty Quantification, Heidelberg Institute for Theoretical Studies, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3655693.3655703

Reference41 articles.

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2. Martin Arjovsky and Léon Bottou. 2017. Towards Principled Methods for Training Generative Adversarial Networks. https://doi.org/10.48550/arXiv.1701.04862 arxiv:1701.04862 [stat.ML]

3. Martin Arjovsky Soumith Chintala and Léon Bottou. 2017. Wasserstein GAN. https://doi.org/10.48550/arXiv.1701.07875 arxiv:1701.07875 [stat.ML]

4. Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, and Konrad Rieck. 2022. Dos and Don’ts of Machine Learning in Computer Security. In 31st USENIX Security Symposium (USENIX Security 22). USENIX Association, Boston, MA, 3971–3988.

5. Interpretable Machine Learning Models for Malicious Domains Detection Using Explainable Artificial Intelligence (XAI)