A Novel DGA Domain Adversarial Sample Generation Method By Geometric Perturbation

Author:

Liu Qihe1,Yu Gao1,Wang Yuanyuan1,Yi Zeng1

Affiliation:

1. University of Electronic Science and Technology of China, China

Funder

Sichuan Science and Technology Program

Publisher

ACM

Reference30 articles.

1. Enoch Agyepong , William  J Buchanan , and Kevin Jones . 2018 . Detection of Algorithmically Generated Malicious Domain. In Conference: 6th International Conference of Advanced Computer Science & Information Technology. Enoch Agyepong, William J Buchanan, and Kevin Jones. 2018. Detection of Algorithmically Generated Malicious Domain. In Conference: 6th International Conference of Advanced Computer Science & Information Technology.

2. Rohan Mahesh Amin. [n.d.]. Detecting targeted malicious email through supervised classification of persistent threat and recipient oriented features. Proquest Llc ([n. d.]). Rohan Mahesh Amin. [n.d.]. Detecting targeted malicious email through supervised classification of persistent threat and recipient oriented features. Proquest Llc ([n. d.]).

3. DeepDGA

4. Shumeet Baluja and Ian Fischer. 2017. Adversarial transformation networks: Learning to generate adversarial examples. arXiv preprint arXiv:1703.09387(2017). Shumeet Baluja and Ian Fischer. 2017. Adversarial transformation networks: Learning to generate adversarial examples. arXiv preprint arXiv:1703.09387(2017).

5. Exposure

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1. Towards Robust Domain Generation Algorithm Classification;Proceedings of the 19th ACM Asia Conference on Computer and Communications Security;2024-07

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