qianyin123/brfast

Browser Fingerprinting Attribute Selection Tool

BrFAST (Browser Fingerprinting Attribute Selection Tool)

Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usages
   • Preparing the publicly available fingerprint datasets
   • Measuring the usability cost of the attributes
   • Running BrFAST from the command line
   • Displaying the visualization of BrFAST as a web application
   • Testing, code coverage and documentation generation
4. Architecture
5. Roadmap
6. How to cite
7. License
8. Contact

About The Project

This repository hosts BrFAST, our Browser Fingerprinting Attribute Selection Tool that we developed to demonstrate our attribute selection framework FPSelect. You can read our publication about FPSelect (also available on arXiv and on HAL).

BrFAST takes the form of a command line and a web application that you can use to select the attributes to implement in your browser fingerprinting probe. You can read our publication about BrFAST (also available in assets/brfast.pdf, on arXiv and on HAL).

Built With

• Modin help us distribute the fingerprint analysis.
• Flask is the framework with which the web application is built.
• Bootstrap help us make beautiful web pages without too much effort.
• D3JS is used for the visualization of the exploration of the possibilities displayed as a graph.

Getting Started

Prerequisites

BrFAST is developed in Python3 for which you should have a version installed on your system. BrFAST was tested on Python3.8 and Python3.9.

Installation

1. Clone this repository.

   git clone https://github.com/tandriamil/BrFAST

2. Create a virtual environment (not mandatory but recommended).

   # The virtual environment is created in a directory named "venv"
   python3 -m venv venv

3. Enter the scope of the virtual environment.

   source venv/bin/activate

4. Install the dependencies.

   pip install -r requirements.txt

5. Copy the config.ini.template to config.ini to use the default configuration. You can edit this file to meet your needs.

   cp config.ini.template config.ini

Usages

You can find below the several usages of BrFAST. Please execute the scripts once the dependencies are installed.

Preparing the publicly available fingerprint datasets

To use the two publicly available browser fingerprint datasets, we provide the necessary assets and instructions in executables/dataset/readme.md.

Measuring the usability cost of the attributes

We pre-compute the usability cost of the attributes and provide these results to our usability cost measures. The scripts to measure these usability costs are in the executables/measures directory. We also provide the resulting measures in the assets/data/[dataset]-measures directories.

Average attribute size

To compute the average size of the attributes of a fingerprint dataset:

python -m executables.measures.memory input_dataset output_file

• input_dataset: The path to the browser fingerprint dataset as a csv file.
• output_file: The path where to save the results to as a csv file.

Average attribute instability

To compute the average instability of the attributes of a fingerprint dataset:

python -m executables.measures.instability input_dataset output_file

• input_dataset: The path to the browser fingerprint dataset as a csv file.
• output_file: The path where to save the results to as a csv file.

Running BrFAST from the command line

The scripts to run BrFAST from the command line are in the executables/experiments directory.

Simple dummy example

The simple dummy example runs the example shown in our FPSelect paper. All the data that is used and the measures are hard-written in the code. You can still execute it to check that BrFAST runs fine.

python -m executables.experiments.dummy_fpselect_example

A single execution

The script named single_execution.py processes a single execution:

python -m executables.experiments.single_execution [options] input_data_dir

The required positional argument input_data_dir should point to a directory that contains the data named as:

• fingerprints.csv for the browser fingerprint dataset file.
• memory.csv for the average size of each attribute.
• instability.csv for the proportion of changes of each attribute.

You can use the optional arguments to specify the parameters:

• -m [selection_method]: The attribute selection method among fpselect (the default one), entropy, and conditional_entropy.
• -t [threshold]: The sensitivity threshold (default is 0.10).
• -k [submissions]: The number of submissions by the considered attacker. This is the k used to measure the proportion of the users that share the k most common fingerprints (default is 4).
• -o [trace_file]: The path where to save the trace file if this option is set.
• -p [paths]: The number of paths explored in the lattice of the possibilities by FPSelect (default is 3).
• --no-pruning: Prevent from using the pruning methods of FPSelect.

The script is configured to the use following measures for the sensitivity and the usability cost. You can copy and edit this script to use other implemented measures or your owns.

• The sensitivity measure used is TopKFingerprints which computes the sensitivity as the proportion of the users that share the top-k fingerprints.
• The usability cost measure is MemoryInstability which measures the average size and number of changing attributes from the browser fingerprints.

Multiple executions

The script named multiple_executions.py processes multiple executions.

python -m executables.experiments.multiple_executions

It takes a list of values for each parameter and processes the exploration considering the Cartesian product of the values of each parameter. You must at least edit the BASE_PATH and check the DATA_DIRECTORIES directories.

The input directories should contain the same files as for the single execution. The same sensitivity and usability cost measures as for the single execution are used.

The resulting traces of these executions are written in the same directory as the input directory of an execution. The name of a trace is composed of the attribute selection method, the sensitivity threshold, the number of submissions by the attacker, and the weights of the usability costs, separated by dashes.

Displaying the visualization of BrFAST as a web application

BrFAST includes a visualization tool that takes the form of a web application. This application proposes two modes: trace replay and real time exploration. We detail below how to start and use the web application.

1. Set the environment variables of Flask:

export FLASK_APP=brfast.webserver.application

# The following is optional and shows verbose logs when errors are encountered
# export FLASK_ENV=debug

# The following is optional and reloads the application after each edit
# export FLASK_ENV=development

2. Run the Flask application:

python -m flask run

# You can specify a port if you run several applications
# python -m flask run --port 5001

3. Open you web browser to the default home page.

4. Choose a mode between trace replay and real time exploration.

5. Fill the form with the necessary information and files. Submit the form when it is done.

Trace replay The trace replay requires a trace file and accepts an optional fingerprint dataset. You can find precomputed traces in the assets/data/[dataset]-traces directories. The fingerprint dataset is used to display additional information about the explored attribute sets.

Real time exploration The real time exploration requires a set of information that are explained in the configuration form. After all the information are verified, the exploration is started in the background and the visualization page is displayed. Note that the attribute sets can be explored faster that what is displayed as the newly explored attribute sets are collected by batch at a fixed frequency.

Visualization page The visualization page displays

• The parameters of the exploration.
• The current state of the exploration with the best solution currently found. A spinner indicates that the exploration is still running. When it is finished, a button to download the trace replaces it.
• The usability gain as the usability cost of the best solution as a percentage of the usability cost of the candidate attributes (i.e., the complete set of attributes).
• The visualization of the exploration with the explored attribute sets. Those that were explored are blue, the starting empty set is light blue, those that were pruned are orange, those that satisfy the sensitivity threshold are green, and the current best solution is green.
• Passing the mouse over an attribute set shows its information, and clicking on it opens a new tab with additional information about this attribute set.

Testing, code coverage and documentation generation

You can execute the tests by:

python -m unittest discover -s tests

You can generate the coverage report in htmlcol (open index.html) by:

coverage run -m unittest discover -s tests
coverage html

You can generate the documentation in html (open brfast/index.html) by:

pdoc --html brfast

Architecture

Below, we describe the content of each module of this project.

assets

The assets directory contains assets related to BrFAST.

• architecture.md describes the architecture of the brfast module.
• data contains the results of the usability cost measures and traces of
  execution for the two publicly available browser fingerprint datasets.
• images contains the images of this document.

brfast

The brfast directory contains the BrFAST module written in Python3.

• config contains the configurations of BrFAST.
• data contains the interfaces and the implementations related to the data types manipulated by BrFAST.
• exploration contains the interfaces and the implementations of the exploration algorithms.
• measures contains the interfaces and the implementations of the different measures used by BrFAST.
• utils contains utility functions used by BrFAST.
• webserver contains the web server application of BrFAST.

Roadmap

The attribute selection tool is under development on a private repository. This one is a public mirror that will include the features when they are ready. The features to come are listed below.

• Implementation of a sensitivity measure by the proportion of the users that share the most common fingerprints.
• Implementation of a usability cost measure that captures the size and the instability of the generated fingerprints.
• Implementation of the usual attribute selection methods that rely on the entropy and the conditional entropy.
• Inclusion of the resources necessary to use the publicly available fingerprint datasets of FPStalker and HTillmann.
• Implementation of the replay of an execution trace with the explored attribute sets as a graph using the D3JS library.
• Implementation of the visualization of the information on the generated fingerprints given an attribute set, displaying their sensitivity, their usability cost, and a sample of the resulting fingerprints.
• Add more insights into the attribute sets: their unicity, their entropy, their stability, and so on.
• Passing the configuration variables in the configuration file.
• Check whether the implementation of parallelism in the measures and the explorations (mostly FPSelect) is more performant or whether the overhead is too heavy in which case we should get back to the sequential way.
• Implementation of the visualization of a real time exploration.

Improvements

• Force the node composed of all the attributes to be attracted to the center instead of being projected to the infinity and beyond.
• Redact the documentation about how to extend BrFAST.
• Reduce the number of displayed nodes for performance reasons.
• Set the precomputed usability cost measures optional and propose to compute them on the fly.
• Increase the coverage of the tests.
• Increase the set of the methods that support modin.
• Implement the exact sensitivity measure used in the FPSelect paper.

How to cite

For a reference to this work, please cite the following paper:

Nampoina Andriamilanto and Tristan Allard. 2021. BrFAST: a Tool to Select Browser Fingerprinting Attributes for Web Authentication According to a Usability-Security Trade-off. In Companion Proceedings of the Web Conference 2021 (WWW ’21 Companion), April 19–23, 2021, Ljubljana, Slovenia. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3442442.3458610

License

Distributed under the MIT License. See the LICENSE file for more information.

Contact

Nampoina Andriamilanto - tompo.andri [at] gmail [dot] com

Acknowledgments

I developed this project when I was a Temporary Teaching and Research Associate at the University of Rennes 1 and at the IRISA laboratory in the DRUID team.