• Semi-supervised-Learning-QUIC-
• • Prerequisites
  • Dataset
  • Data Input format
  • To reproduve our results

Semi-supervised-Learning-QUIC-

Implementation of "How to Achieve High Classification Accuracy with Just a Few Labels: A Semi-supervised Approach Using Sampled Packets". For more information read the paper.

Prerequisites

Python 3 and Pytorch

Dataset

Dataset is available in QUIC Dataset

Data Input format

It is assumed that each flow is converted into a text file containing 4 columns: Timestamp, relative time (from the first packet in the flow), packet lenght, direction.
Statistical features are calculated in dataProcessInMemoryQUIC.py file.

To reproduve our results

1. Run dataProcessInMemoryQUIC.py to do the pre-processing and caculating statistical features.
2. Run pre-training.py to train the model to predict statistical features.
3. Run re-training to transfer the weight from previous step and re-train the model to predict class labels.