FedETC: Encrypted traffic classification based on federated learning

Abstract

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 one-dimensional 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.