Learning from imbalanced data for encrypted traffic identification problem

@inproceedings{Vu2016LearningFI,
  title={Learning from imbalanced data for encrypted traffic identification problem},
  author={Ly Vu and Dong Van Tra and Nguyen Quang Uy},
  booktitle={SoICT},
  year={2016}
}
Identifying encrypted application traffic is an important issue for many network tasks including quality of service, firewall enforcement and security. One of the challenging problems of classifying encrypted application traffic is the imbalanced property of network data. Usually, the amount of unencrypted traffic is much higher than the amount of encrypted traffic. To date, the machine learning based approach for identifying encrypted traffic often solely focused on examining and improving… CONTINUE READING