⚠️ ⚠️ ⚠️ Experimental - PLEASE BE CAREFUL. Intended for Reasearch purposes ONLY. ⚠️ ⚠️ ⚠️

This repository contains code and data of the paper Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces, published in IEEE Transactions on Information Forensics and Security (TIFS). The preprint version of the paper is available at: arXiv version. Mockingbird is designed to work against deep-learning-based website fingerprinting attacks. Extensive evaluation shows that Mockingbird is effective against both white-box and black-box attacks including a more advanced intersection attacks.

Reference Format

@article{rahman2020mockingbird,
      title={{Mockingbird:} Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces}, 
      author={Mohammad Saidur Rahman and Mohsen Imani and Nate Mathews and Matthew Wright},
      year={2020},
      eprint={1902.06626},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}

Dependencies & Required Packages

Ensuring all the depencies is critical. It is hard to keep all the packages updated at once. Some versions might be relaitvely old. So we suggest the users to create a python virtual environment or conda environment and install the required packages.

Please make sure you have all the dependencies available and installed.

• NVIDIA GPU should be installed in the machine, running on CPU will significantly increase time complexity.
• Ubuntu 16.04.5/ CentOS Linux 7
• Python3-venv/ conda
• Keras version: 2.2.4
• TensorFlow version: 1.15.0
• Numpy: 1.16.6
• Matplotlib: 2.2.5
• CUDA Version: 10.2
• CuDNN Version: 7
• Python Version: 2.7.5

-- Please install the required packages using the following command: pip install -r requirements.txt

Dataset

We have shared the processed data using a Google Drive. Please download the processed data from this Google Drive URL. After downloading, please put the data into the dataset directory.

Adversarial Trace Generation using Mockingbird

Running Attack with Adversarial Training

Questions, Comments, & Feedback

Please, address any questions, comments, or feedback to the authors of the paper. The main developers of this code are:

• Mohammad Saidur Rahman ([email protected])
• Mohsen Imani ([email protected])
• Nate Mathews ([email protected])
• Matthew Wright ([email protected])

Acknowledgements

This material is based upon work supported in part by the National Science Foundation (NSF) under Grants No. 1423163, 1722743, 1816851, and 1433736.