Deep Learning-Based Encrypted Network Traffic Classification and Resource Allocation in SDN

Authors

  • Hao Wu School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
  • Xi Zhang School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
  • Jufeng Yang Signal and Communication Research Institute, China Academy of Railway Science Corporation, Beijing, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2085

Keywords:

Deep learning; Encrypted traffic; Fourier transform; Convolutional neural network; DFR architecture

Abstract

In the rapid development of network technology, with the improvement of the quality and quantity of network users’ demands, more and more network information technology and excessive network traffic also raise people’s attention to the internal network security. Especially for the classification and resource allocation of encrypted network traffic, the research of related technologies has become the main research direction of the development of network technology. The extensive application of deep learning provides a new idea for the study of traffic classification. Therefore, on the basis of understanding the current situation, the improved convolutional neural network is selected to conduct an in-depth discussion on traffic classification and resource allocation of encrypted networks based on deep learning. The performance of the system is verified from the perspective of practical application.

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References

Pan W, Feng Y, Chen X, et al. DataNet: Deep Learning based Encrypted Network Traffic Classification in SDN Home Gateway[J]. IEEE Access, 2018, PP(99):1–1.

Sengan S, Setiawan R, Ganga R R, et al. Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network[J]. Wireless Personal Communications, 2021(1).

Xu J, Wang J, Qi Q, et al. Deep Neural Networks for Application Awareness in SDN-based Network[C]// 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2018.

Chang L H, Lee T H, Chu H C, et al. Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks[J]. Advances in Technology Innovation, 2020.

Lopes F A, Santos M, Fidalgo R, et al. A Software Engineering Perspective on SDN Programmability[J]. IEEE Communications Surveys & Tutorials, 2016, 18(2):1255–1272.

Niyaz Q, Sun W, Javaid A Y. A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN)[J]. Security & Safety, 2016, 4(12).

Giotis K, Argyropoulos C, Androulidakis G, et al. Combining OpenFlow and sFlow for an effective and scalable anomaly detection and mitigation mechanism on SDN environments[J]. Computer Networks, 2014, 62:122–136.

Tian, Shiming, Gong, et al. End-to-end encrypted network traffic classification method based on deep learning[J]. The Journal of China Universities of Posts and Telecommunications, 2020, v.27(03):25–34.

Zhang C, Wang X, F Li, et al. Deep learning–based network application classification for SDN[J]. Transactions on Emerging Telecommunications Technologies, 2017:e3302.

Abbasi M, Shahraki A, Taherkordi A. Deep learning for Network Traffic Monitoring and Analysis (NTMA): A survey[J]. Computer Communications, 2021(3).

Guang Chen, Weiliang Han, Wenzhi Zhang., Deep Learning-Based Encrypted Traffic Classification and Intrusion Detection [J]. Computer Measurement and Control, 2020, v.28; No.256 (01): 59–65.

Lee J T, Chung Y. Deep Learning-Based Vehicle Classification Using an Ensemble of Local Expert and Global Networks. IEEE, 2017:920–925.

J Wan, Wu L, Xia Y, et al. Classification Method of Encrypted Traffic Based on Deep Neural Network[C]// 2019:P.54544.

Hou L, Luo X Y, Wang Z Y, et al. Representation learning via a semi-supervised stacked distance autoencoder for image classification[J]. Information and Electronic Engineering Frontier: English Edition, 21(7): 14.

Wubin Pan, Guang Cheng, Xiaojun Guo, et al. Review and Outlook of Network Encryption Traffic Identification Research [J]. Communications

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Published

2021-11-19

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Section

Articles