Privacy Preserving Collaborative Encrypted Network Traffic Classification

Interconnecting the following works:

• Differential Privacy
• Federated Learning (We plan to add split learning)
• Membership Inference Attacks
• Encrypted Traffic Classification

PAPER: https://www.researchgate.net/profile/Ezzeldin-Tahoun/publication/345974499_PrivPkt_Privacy_Preserving_Collaborative_Encrypted_Traffic_Classification/links/5fb378d592851cf24cd85891/PrivPkt-Privacy-Preserving-Collaborative-Encrypted-Traffic-Classification.pdf

We utilize Federated Averaging to enable the collaborative learning setting.

Ref: https://arxiv.org/abs/1602.05629

We make use of DPSGD to ensure a ceratin level of privacy.

Ref:https://arxiv.org/abs/1602.05629

We make use of Shokri et al. Membership Inference Attacks to evaluate our mitigations.

Ref: https://arxiv.org/abs/1610.05820

We tackle the problem of Encrypted Traffic Classification. We utilize the work of DeepPacket and use the ISCX Vpn 2016 Dataset to evaluate our work.

Ref: https://arxiv.org/abs/1709.02656

Ref: https://www.unb.ca/cic/datasets/vpn.html