Modeling Website Fingerprinting in Open and Closed Worlds

💡Introduction

The goal of this project is to create machine learning models using Tor network packet flow data, to determine whether an instance is communicating with a monitored website or an unmonitored website, and to identify its destination if it is a monitored website.

🖥️ Closed-world scenario

In the closed-world experiments, the user can only access monitored(preivously-known) websites.
The goal is to classify the 95 monitored websites.
We used an SVM, a decision tree, and a random forest model.

🖥️ Open-world scenario

In the open-world experiments, the user to access any websites within the system.
Data can be classified into two parts

• monitored data : the attacker is interested in
• unmonitored data : deemed irrelevant by the attacker
  

monitored website instances are treated as positive samples, and unmonitored website instances are treated as negative samples.

📌 Binary Classification

Determine whether the web traffic trace corresponds to a monitored website.
To do this, we reassign the label '1' to all monitored website instances (positive samples) and assign the label '-1' to all unmonitored website instances (negative samples)

📌 Multi-Class Classification

Classify 95 monitored website traces with unique labels against additional unmonitored websites.
In the multi-class setting, we label the monitored website instances with {0, 1, 2, ..., 94} and the unmonitored website instances with the label '-1'.

We used a decision tree and a random forest model.

💡 How to RUN

You can download monitored and unmonitored data from the below google drive.
[dataset] (https://drive.google.com/drive/folders/13sDplxKUNmntbYr6WhpqQARiBvH41Oum)
You can run the code in Colab. Please upload the downloaded data to Colab's file.

‼️ You need to replace the path in this code with the absolute path of the files mon_standard10.pkl and unmon_standard10_3000.pkl on your drive ‼️

with open("/content/sample_data/mon_standard.pkl", "rb") as file: 

with open("/content/sample_data/unmon_standard10_3000.pkl", "rb") as file: