This repository contains the implementation of adversarial examples of network traffic in website fingerprinting. We implemented four differenct adversarial example generation methods, like FGSM, DeepFool, PGD and advGAN, orginally introduced in image domain, we customized them to apply on network traffic.
** The data set and code for research purpose only**
This repository contains separate directories for the attacks, train, tools and nni_search. A brief description of the contents of these directories is below. More detailed usage instructions are found in the individual directories' README.md.
The attack directory contains the code for generating adversarial examples with different algorithms.
The train directory contains the code for the deep learning based website fingerprinting models, GAN model, testing functions, data preparation, and some related utility functions.
The tools directory contains the code for data preprocessing, evaluations, detecting adversary with different models.
The nni_search directory contains the code for hyper-perameters search for CNN and LSTM models by using NNI tool.
The dataset we used is collected by Sirinam et.al. It includes 95 monitored websites with 1,000 traces per website for the closed world evaluation and 40,716 unmonitored websites with 1 trace per website for the open-workd evaluation. You can find more details here "https://github.com/deep-fingerprinting/df" .
This project is entirely written in Python 3, based on PyTorch 1.3.0. We recommend you to run it with a GPU machine.
See the project's directories for usage information.
Hao Liu, [email protected]
Boyang Wang, [email protected]