Open Access
Issue
JNWPU
Volume 38, Number 5, October 2020
Page(s) 1129 - 1137
DOI https://doi.org/10.1051/jnwpu/20203851129
Published online 08 December 2020
  1. Huo Dajun. Operation of Network Swarm[M]. Beijing: National Defense University Press, 2013 [Google Scholar]
  2. Liang Yixin, Cheng Guang, Guo Xiaojun. Research Progress on Architecture and Protocol Stack of the Airborne Network[J]. Journal of Software, 2016, 27 (1): 96– 111 [Article] (in Chinese) [Google Scholar]
  3. Nguyen T T T, Armitage G. A Survey of Techniques for Internet Traffic Classification Using Machine Learning[J]. IEEE Communications Surveys & Tutorials, 2009, 10 (4): 56– 76 [Article] [CrossRef] [Google Scholar]
  4. Moore A W, Zuev D. Internet Traffic Classification Using Bayesian Analysis Techniques[J]. ACM Sigmetrics Performance Evaluation Review, 2005, 33 1: 50 [Article] [CrossRef] [Google Scholar]
  5. Wu K, Ke J. A Scheme of Real-Time Traffic Classification in Secure Access of Power Enterprise Based on Improved Naïve Bayesian Classification Algorithm[C]//IEEE International Conference on Software Engineering & Service Science, 2017 [Google Scholar]
  6. Xu Peng, Lin Sen. Internet Traffic Classification Using C4.5 Decision Tree[J]. Journal of Software, 2009, 20 (10): 2692– 2704 [Article] (in Chinese) [CrossRef] [Google Scholar]
  7. Tong Da, Qu Y R, Prasanna V K. Accelerating Decision Tree Based Traffic Classification on FPGA and Multicore Platforms[J]. IEEE Trans on Parallel & Distributed Systems, 2017, 28 (11): 3046– 3059 [Article] [CrossRef] [Google Scholar]
  8. Cao Jie, Fang Zhiyi, Qu Guannan, et al. An Accurate Traffic Classification Model Based on Support Vector Machines[J]. Networks, 2017, 27 (1): e1962 [Article] [Google Scholar]
  9. Sun Guanglu, Chen Teng, Su Yangyang, et al. Internet Traffic Classification Based on Incremental Support Vector Machines[J]. Mobile Networks & Applications, 2018, 23 (14): 1– 8 [Article] [CrossRef] [Google Scholar]
  10. Wang Wei, Zhu Ming, Zeng Xuewen. Malware Traffic Classification Using Convolutional Neural Network for Representation Learning[C]//2017 International Conference on Information Networking(ICOIN), 2017: 712–717 [Google Scholar]
  11. Wang Wei, Zhu Ming, Wang Jinlin, et al. End-to-End Encrypted Traffic Classification with One Dimensional Convolution Neural Networks[C]//2017 IEEE International Conference on Intelligence and Security Informatics(ISI), 2017 [Google Scholar]
  12. Wang Yong, Zhou Huiyi, Feng Hao, et al. Network Traffic Classification Method Basing on CNN[J]. Journal on Communications, 2018, 39 (1): 14– 23 [Article]

    WANG Yong, ZHOU Huiyi, FENG Hao, et al. Network Traffic Classification Method Basing on CNN[J]. Journal on Communications, 2018, 39(1):14-23

    (in Chinese) [Google Scholar]
  13. Pan Sinno Jialin, Yang Qiang. A Survey on Transfer Learning[J]. IEEE Trans on Knowledge & Data Engineering, 2010, 22 (10): 1345– 1359 [Article] [Google Scholar]
  14. Shin H C, Roth H R, Gao M, et al. Deep Convolutional Neural Networks for Computer Aided Detection:CNN Architectures, Dataset Characteristics and Transfer Learning[J]. IEEE Trans on Medical Imaging, 2016, 1 (1): 1285– 1298 [Article] [CrossRef] [Google Scholar]
  15. Zhou Feiyan, Jin Linpeng, Dong Jun. Review of Convolutional Neural Network[J]. Chinese Journal of Computers, 2017, 40 (6): 1– 23 [Article] (in Chinese) [Google Scholar]
  16. Li Qin, Shi Wei, Sun Jieping, et al. The Research of Network Traffic Identification Based on Convolutional Neural Network[J]. Journal of Sichuan University, 2017, 54 (5): 959– 964 [Article] (in Chinese) [Google Scholar]
  17. Sharif razavian A, Azizpour H, Sullivan J, et al. CNN Feature Off-the-Shelf: an Astounding Baseline for Recognition[C]//Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, UH, USA, 2014: 806–813 [Google Scholar]
  18. Jason Yosinski, Jeff Clune, Yoshua Bengio, et al. How Transferable Are Features in Deep Neural Networks?[C]//Advances in Neural Information Processing System 27, Montreal, Canada, 2014: 3320–3328 [Google Scholar]

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