CL-ETC: A Contrastive Learning Method for Encrypted Traffic Classification
Author:
Affiliation:
1. Institute for Network Sciences and Cyberspace, BNRist, Tsinghua University,Beijing,China
2. College of Mathematics and Computer Science, Fuzhou University,Fuzhou,China
Funder
National Key Research and Development Program of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9829715/9829755/09829767.pdf?arnumber=9829767
Reference25 articles.
1. Malware traffic classification using convolutional neural network for representation learning
2. End-to-end encrypted traffic classification with one-dimensional convolution neural networks
3. App-Net: A Hybrid Neural Network for Encrypted Mobile Traffic Classification
4. The applications of deep learning on traffic identification;wang;BlackHat USA,2015
5. Datanet: Deep Learning Based Encrypted Network Traffic Classification in SDN Home Gateway
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1. A balanced supervised contrastive learning-based method for encrypted network traffic classification;Computers & Security;2024-10
2. CoMDet: A Contrastive Multimodal Pre-Training Approach to Encrypted Malicious Traffic Detection;2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC);2024-07-02
3. Self-Supervised Traffic Classification: Flow Embedding and Few-Shot Solutions;IEEE Transactions on Network and Service Management;2024-06
4. A Novel Self-Supervised Framework Based on Masked Autoencoder for Traffic Classification;IEEE/ACM Transactions on Networking;2024-06
5. Self-Supervised Learning Malware Traffic Classification Based on Masked Autoencoder;IEEE Internet of Things Journal;2024-05-15
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