IDSGAN: Generative Adversarial Networks for Attack Generation Against Intrusion Detection-Reference-Cited by-同舟云学术

IDSGAN: Generative Adversarial Networks for Attack Generation Against Intrusion Detection

Published:2022 Issue: Volume: Page:79-91
ISSN:0302-9743
Container-title:Advances in Knowledge Discovery and Data Mining
language:
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Author:

Lin Zilong,Shi Yong,Xue Zhi

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-05981-0_7

Reference15 articles.

1. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223. PMLR (2017)

2. Carlini, N., Wagner, D.: Adversarial examples are not easily detected: bypassing ten detection methods. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 3–14. ACM, Dallas, TX, USA (2017)

3. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

4. Lecture Notes in Computer Science;B Hitaj,2019

5. Hu, W., Tan, Y.: Generating adversarial malware examples for black-box attacks based on gan. arXiv:1702.05983 (2017)

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