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FedETC: Encrypted traffic classification based on federated learning
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DOI：10.1016/j.heliyon.2024.e35962.png)
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摘要
En 中文
The current popular traffic classification methods based on feature engineering and machine learning are difficult to obtain suitable traffic feature sets for multiple traffic classification tasks. Besides, data privacy policies prohibit network operators from collecting and sharing traffic data that might compromise user privacy. To address these challenges, we propose FedETC, a federated learning framework that allows multiple participants to learn global traffic classifiers, while keeping locally encrypted traffic invisible to other participants. In addition, FedETC adopts onedimensional convolutional neural network as the base model, which avoids manual traffic feature design. In the experiments, we evaluate the FedETC framework for the tasks of both application identification and traffic characterization in a publicly available real-world dataset. The results show that FedETC can achieve promising accuracy rates that are close to centralized learning schemes.
Keyword:
Network traffic classification
Federated learning
Encrypted traffic
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期刊
IF:
3.6
论文数:
3.8W
被引数:
10.5W
机构
引用论文
Deep-Full-Range: A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection FrameworkDeep-Full-Range: 基于深度学习的网络加密流量分类和入侵检测框架.png)
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IEEE ACCESSIF3.6
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