Synthetic Network Traffic Data Generation and Classification of Advanced Persistent Threat Samples: A Case Study with GANs and XGBoost-Reference-Cited by-同舟云学术

Synthetic Network Traffic Data Generation and Classification of Advanced Persistent Threat Samples: A Case Study with GANs and XGBoost

Published:2023 Issue: Volume: Page:1-18
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
Short-container-title:

Author:

Anande T. J.,Leeson M. S.^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-39059-3_1

Reference55 articles.

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3. Alqahtani, S.H., Thorne, M.K., Kumar, G.: Applications of generative adversarial networks (GANs): an updated review. Arch. Comput. Methods Eng. 28(2), 525–552 (2021)

4. Alshamrani, A., Myneni, S., Chowdhary, A., Huang, D.: A survey on advanced persistent threats: techniques, solutions, challenges, and research opportunities. IEEE Commun. Surv. Tutor. 21(2), 1851–1877 (2019)

5. Anande, T.J., Leeson, M.S.: Generative adversarial networks (GANs): a survey on network traffic generation. Int. J. Mach. Learn. Comput. 12(6), 333–343 (2022)

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