GAN-N-Net Project README

Overview

The GAN-N-Net repo contains code written as part of my research to provide a method for reproducing the results presented in the associated paper. The codebase includes implementation of models with and without the proposed GAN-N-Net model enhancements.

Repository Structure

• mini.py: Script to obtain results using our GAN-N-Net model.
• mini100.py: Script to obtain results without using our GAN-N-Net model.

Getting Started

To reproduce the paper results, follow these steps:

Prerequisites

Ensure you have the requairements package installed you can use requirements.txt file pip install -r requirements.txt

Installation

Clone the repository to your local machine:

git clone https://github.com/[username]/GAN-N-Net.git

cd GAN-N-Net

pip install -r requirements.txt

To get results with our model:

for QUIC Paris-Est Créteil(quic_pcaps), run:

python mini.py GAN-N-Net/datasets/mini_flowpic_quic_pcaps 

for QUIC Berkeley (quic text), run:

python mini.py GAN-N-Net/datasets/mini_flowpic_quic_text

To get results without our model

For QUIC Paris-Est Créteil(quic_pcaps), run:

python mini100.py GAN-N-Net/datasets/mini_flowpic_quic_pcaps 

For QUIC Berkeley (quic text), run:

python mini100.py GAN-N-Net/datasets/mini_flowpic_quic_text

You can also set a unique name for your traning.

For example:

python mini.py GAN-N-Net/datasets/mini_flowpic_quic_pcaps try_gannet

This is a list of all available Arguments

mini.py & mini100.py accepts the following arguments to customize its behavior:

• data_dir (Required): The directory containing your training data.

• run_name (Optional): A name for this specific run of the script. This name will be used in log files. If not provided, the data folder name will be used.

• --test_split (Optional): The fraction of the data to allocate for the test set (e.g., 0.2 represents 20%). Default is 0.1 (10%).

• --val_split (Optional): The fraction of the data to allocate for the validation set. Default is 0.3 (30%).

• --batch_size (Optional): The batch size used during training. Default is 64.

Example Usage:

python your_script_name.py /path/to/data/ my_training_run --test_split 0.25