ZuoYuanP / wf-in-the-age-of-quic

This repository contains the code for the paper "Website Fingerprinting in the Age of QUIC" (PETS 2021).

Website Fingerprinting in the Age of QUIC

代码来自论文 "Website Fingerprinting in the Age of QUIC" (PETS 2021).

代码被分成workflows,一个workflows负责一个或几个相关实验， 每个workflows都包含用于收集和处理数据、执行机器学习分类以及为论文生成图表的脚本。

Data Availability Statements

The data used to support this paper are provided in two sets:

• quic-wf-core.tgz (831 MB):
  • 用于扫描的域名和以CSV格式提供的带有headers的扫描结果；
  • 具有class、protocol和 VPN 位置标签的 HDF5 格式的数据集; size和timestamp数组；低于175字节的数据包被移除；
• quic-wf-raw.tar (28 GB):
  • 原始获取的QUIC和TCP跟踪及其关联的元数据.
  • 每个文件都是一个JSON对象流，包含以下可能为空值的键:
    • url, final_url: requested and final redirected URLs
    • status: HTTP status code of the fetch
    • protocol: protocol used to request the main page, "quic" or "tcp"
    • packets: base64 encoded PCAP for the request
    • http_trace: Chromium DevTools performance log (reference)
  • 由于大小限制，此内容仅根据请求提供。.
  • New 2022-01-24: Also available in pcapml format upon request!

Note: 数据集中的QUIC trace是指一种包含了QUIC和TCP数据包的Wireguard隧道的跟踪，这些数据包与通过QUIC连接请求网页相关。这意味着至少与Web服务器建立的初始连接是通过QUIC完成的

Computational Requirements

Software Requirements

• Bash
• Git and Git-LFS 2.17
• Python 3.7
  • Dependencies listed in requirements.txt
  • The wf-tools library
• Optional:
  • Docker 19.03.12
  • Wireguard v1.0.20200513
  • docker-machine v0.16.2

Memory and Runtime Requirements

该代码最后在一个计算集群上运行，每个实验在2-4个核心上运行（每个核心2.4 GHz），每个核心配备6 GB主内存。 机器学习的训练和测试使用了额外的0-2个基于分类器的GPU。 为了减少运行时间，这些作业是并行运行的，每个训练-测试拆分需要1-3小时。

Note: 当前的 requirements.txt 文件指定了使用 tensorflow-cpu。如果你有 GPU 可以使用的话，请安装 tensorflow-gpu 替代.

Getting Started

一个包含已下载代码和数据的虚拟机镜像当前可通过此 link快速访问.

下说明描述了如何从头开始设置和运行workflows.

1. Clone the repository and change to the directory.

# Clone the repository
git clone https://github.com/jpcsmith/wf-in-the-age-of-quic.git
# Change to the code directory
cd wf-in-the-age-of-quic/
# Download the git LFS files
git lfs pull

2. Create and activate a virtual environment

python3 -m venv env
source env/bin/activate

3. Install the Python requirements

# Ensure that pip is the latest version
python3 -m pip install --upgrade pip

# Install the requirements using pip
python3 -m pip install --no-cache-dir -r requirements.txt

If the installation fails, ensure that the Python development libraries are installed and retry the above. On Ubuntu 18.04, this would be the python3.7-dev and python3-venv packages.

4. (Optional) Download and extract the data

wget https://polybox.ethz.ch/index.php/s/u10mAN6NCcDP39U/download -O quic-wf-core.tgz
tar -xzvf quic-wf-core.tgz

5. (Optional) Install Docker and Wireguard

If planning to run trace-collection, i.e. from the Fetch QUIC Traces workflow, install docker (19.03.12) and Wireguard (v1.0.20200513).

6. Run the desired workflow

Change to the desired workflow's directory and follow the instructions for running the workflow.

workflows和论文的对应章节

Paper Section	Workflows	Directories
4. Combined QUIC-TCP Dataset	Identify QUIC Sites Fetch QUIC Traces	workflows/identify-quic-sites workflows/fetch-any-quic
6. From TCP to QUIC	Generalisability Analysis Single and Mixed Analyses	workflows/generalisability-analysis workflows/single-and-mixed-analyses
7. Joint Classification of QUIC and TCP	Single and Mixed Analyses Distinguish Protocol	workflows/single-and-mixed-analyses workflows/distinguish-protocol
8. Remove Control Packets	Removing Control Packets	workflows/removing-control-packets

List of Tables and Programs

下表列出了负责论文中各个表格和图形的程序和文件。 Notebooks位于notebooks/目录中，输出文件位于results/plots目录中 relative to the associated workflow.

Figure/Table	Workflow	Notebook	Output file
Table 2	Generalisability Analysis	confusion-matrix.ipynb	confusion-matrix.tex
Figure 2	Generalisability Analysis	result-analysis.ipynb	score-vs-quic-presence.pgf
Figure 3	Single and Mixed Analyses	feature-analysis.ipynb	feature-rank-comparison.pgf
Figure 4	Single and Mixed Analyses	determine-num-quic-features.ipynb	quic-feature-scores.pgf
Figure 5	Distinguish Protocol	split-classify.ipynb	split-classify.pgf
Figure 6	Distinguish Protocol	distinguisher-performance.ipynb	distinguisher-performance.pgf
Figure 7	Distinguish Protocol	distinguisher-performance.ipynb	distinguisher-importance.pgf
Figure 8	Removing Control Packets	min-size-analysis.ipynb	packet-size-ecdf.pgf
Table 3	Removing Control Packets	min-size-analysis.ipynb	embedded in notebook
Figure 9	Fetch QUIC Traces	resource-distribution.ipynb	quic-resource-dist.pgf
Figure 10	Generalisability Analysis	vary-deploy.ipynb	vary-deployment.pgf
Figure 11a	Generalisability Analysis	result-analysis-curve.ipynb	quic-presence-prcurve.pgf
Figure 11b	Distinguish Protocol	split-classify.ipynb	split-classify-prcurve.pgf
Figure 11c	Removing Control Packets	min-size-analysis-curve.ipynb	score-vs-min-packet-size-prcurve.pgf

Licence

The code and associated data is released under an MIT licence as found in the LICENCE file.