blackvvine / PrivPkt

Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)

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PrivPkt

Privacy Preserving Collaborative Encrypted Network Traffic Classification

Interconnecting the following works:

  • Differential Privacy
  • Federated Learning
  • Membership Inference Attacks
  • Encrypted Traffic Classification

contributions welcome HitCount




Federated Learning

Our Collaborative Architecture

We utilize Federated Averaging to enable the collaborative learning setting.

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




Differential Privacy

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

DPSGD Algorithm

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




Membership Inference Attacks

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

Membership Inference Attack Architecture

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




Encrypted Traffic Classification

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.

DeepPacket Architecture

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

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

About

Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)

License:MIT License


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Language:Python 100.0%