CETP: A novel semi-supervised framework based on contrastive pre-training for imbalanced encrypted traffic classification

Author:

Lin XinjieORCID,He Longtao,Gou Gaopeng,Yu Jing,Guan Zhong,Li Xiang,Guo Juncheng,Xiong Gang

Funder

Beijing Municipal Science and Technology Commission

National Key Research and Development Program of China Stem Cell and Translational Research

Publisher

Elsevier BV

Reference55 articles.

1. Al-Naami, K., Chandra, S., Mustafa, A., Khan, L., Lin, Z., Hamlen, K., Thuraisingham, B., 2016. Adaptive encrypted traffic fingerprinting with bi-directional dependence. In: Proceedings of the 32nd Annual Conference on Computer Security Applications. ACSAC 2016, pp. 177–188.

2. Amina, S., Bayati, A., Nguyen, K., Cheriet, M., 2018. Featuring Real-Time Imbalanced Network Traffic Classification. In: IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IThings/GreenCom/CPSCom/SmartData 2018, pp. 840–846.

3. Layer normalization;Ba,2016

4. SMOTE: synthetic minority over-sampling technique;Chawla;J. Artif. Intell. Res.,2002

5. Machine learning based mobile malware detection using highly imbalanced network traffic;Chen;Inform. Sci.,2018

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