EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 31 / No 1 (February 2026) Wuhan Univ. J. Nat. Sci., 31 1 (2026) 25-34 References
Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 31, Number 1, February 2026
Page(s) 25 - 34
DOI https://doi.org/10.1051/wujns/2026311025
Published online 06 March 2026
  1. Jiang J C, Ma H T, Ren D E, et al. A survey of intrusion detection research on network security[J]. Journal of Software, 2000, 11(11): 1460-1466(Ch). [Google Scholar]
  2. Tao X M, Zheng Y J, Chen W, et al. SVDD-based weighted oversampling technique for imbalanced and overlapped dataset learning[J]. Information Sciences, 2022, 588: 13-51. [Google Scholar]
  3. Zhang R, Zhang Z Q, Wang D. RFCL: A new under-sampling method of reducing the degree of imbalance and overlap[J]. Pattern Analysis and Applications, 2021, 24(2): 641-654. [Google Scholar]
  4. Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: Synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16: 321-357. [CrossRef] [Google Scholar]
  5. Wang Z, Chu X, Li D D, et al. Cost-sensitive matrixized classification learning with information entropy[J]. Applied Soft Computing, 2022, 116: 108266. [Google Scholar]
  6. Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Neural Information Processing Systems, 2014: 27. [Google Scholar]
  7. Xu L, Skoularidou M, Cuesta-Infante A, et al. Modeling tabular data using conditional GAN[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019: 7335-7345. [Google Scholar]
  8. Ding H W, Chen L Y, Dong L, et al. Imbalanced data classification: A KNN and generative adversarial networks-based hybrid approach for intrusion detection[J]. Future Generation Computer Systems, 2022, 131: 240-254. [Google Scholar]
  9. Ding H W, Sun Y, Huang N N, et al. TMG-GAN: Generative adversarial networks-based imbalanced learning for network intrusion detection[J]. IEEE Transactions on Information Forensics and Security, 2023, 19: 1156-1167. [Google Scholar]
  10. Huang N, Gokaslan A, Kuleshov V, et al. The GAN is dead; long live the GAN! A modern GAN baseline[J]. Advances in Neural Information Processing Systems, 2024, 37: 44177-44215. [Google Scholar]
  11. Wei Z, Li C L, Shen Y A, et al. Thick cloud region content generation of UAV image based on two-stage model[J]. Chinese Journal of Computers, 2021, 44(11): 2233-2247(Ch). [Google Scholar]
  12. Mirza M, Osindero S. Conditional generative adversarial nets[EB/OL]. [2014-12-28]. arXiv:1411.1784. [Google Scholar]
  13. Arjovsky M, Chintala S, Bottou L. Wasserstein generative adversarial networks[C]//International Conference on Machine Learning. New York: ACM, 2017, 70: 214-223. [Google Scholar]
  14. Odena A, Olah C, Shlens J. Conditional image synthesis with auxiliary classifier GANs[C] //34th International Conference on Machine Learning. Sydney: ICML, 2017, 6: 2642-2651. [Google Scholar]
  15. Chen F J, Zhu F, Wu Q X, et al. A survey of generative adversarial networks and their applications in image generation[J]. Chinese Journal of Computers, 2021, 44(2): 347-369(Ch). [Google Scholar]
  16. Vuttipittayamongkol P, Elyan E, Petrovski A. On the class overlap problem in imbalanced data classification[J]. Knowledge-Based Systems, 2021, 212: 106631. [Google Scholar]
  17. Croitoru F A, Hondru V, Ionescu R T, et al. Diffusion models in vision: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(9): 10850-10869. [Google Scholar]
  18. Kotelnikov A, Baranchuk D, Rubachev I, et al. TabDDPM: Modelling tabular data with diffusion models[C]//Proceedings of the 40th International Conference on Machine Learning. Hawaii: PMLR, 2023: 17564-17579. [Google Scholar]
  19. Moustafa N. A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets[J]. Sustainable Cities and Society, 2021, 72: 102994. [Google Scholar]
  20. Ferrag M A, Friha O, Hamouda D, et al. Edge-IIoTset: A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning[J]. IEEE Access, 2022, 10: 40281-40306. [CrossRef] [Google Scholar]
  21. Jolicoeur-Martineau A. The relativistic discriminator: A key element missing from standard GAN[C]//International Conference on Learning Representations. New Orleans: OpenReview.net, 2019: 8622-8647. [Google Scholar]
  22. Miyato T, Kataoka T, Koyama M, et al. Spectral normalization for generative adversarial networks[C]//International Conference on Learning Representations. Vancouver: OpenReview.net, 2018: 376-401. [Google Scholar]
  23. Xu K Y Z, Lu Y J, Wang Z Y, et al. A survey of adversarial examples in computer vision: Attack, defense, and beyond[J]. Wuhan Univ J of Nat Sci, 2025, 30(1): 1-20. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.