ForensiX

Google Chrome forensic tool

Forensic tool for processing, analyzing and visually presenting Google Chrome artifacts.

Features

• Mounting of volume with Google Chrome data and preserving integrity trough manipulation process
  • read only
  • hash checking
• Suspect profile and behavior estimations including:
  • personal information (emails, phone nums, date of birth, gender, nation, city, adress...)
  • Chrome metadata
    • Accounts
    • Version
  • Target system metadata
    • Operating system
    • Display resolution
    • Mobile Devices
  • Browsing history URL category classification using ML model
  • Login data frequency (most used emails and credentials)
  • Browsing activity during time periods (heatmap, barchart)
  • Most visited websites
• Browsing history
  • transition types
  • visit durations
  • avg. visit duration for most common sites
• Login data (including parsed metadata)
• Autofills
  • estimated cities and zip codes
  • estimated phone number
  • other possible addresses
  • geolocation API (needed to be registered to Google)
• Downloads (including default download directory, download statistics...)

• default download directory
• download statistics

• Bookmarks
• Favicons (including all subdomains used for respective favicon)
• Cache
  • URLs
  • content types
  • payloads (images or base64)
  • additional parsed metadata
• Volume
  • volume structure data (visual, JSON)
• Shared database to save potential evidence found by investigators

Installation

Requirements

• Docker
• Docker Compose

Quick Start

1. Clone the repository:

   git clone https://github.com/ChmaraX/forensix.git
   cd forensix

2. Prepare your browser data: Copy your Chrome/Brave browser data to the data directory:

   # For Chrome (replace with your actual profile path)
   cp -r "/Users/username/Library/Application Support/Google/Chrome/Default/." ./data/
   
   # For Brave (replace with your actual profile path)
   cp -r "/Users/username/Library/Application Support/BraveSoftware/Brave-Browser/Profile 2/." ./data/

3. Build and start the application:

   docker-compose up --build

That's it! The Docker setup will automatically:

• Build all services from source code
• Install Node.js and Python dependencies
• Download the ML model (~700MB) for URL classification
• Start all services

Note: The first build may take several minutes due to downloading dependencies and the ML model.

Manual Installation (Alternative)

If you prefer to run without Docker:

1. Install Python dependencies:

   pip install -r requirements.txt

2. Download the ML model:

   ./download-model.sh

3. Install and start services manually:

   # Server
   cd server
   npm install
   npm start
   
   # Client (in another terminal)
   cd client
   npm install
   npm start

The runninng services are listenning on:

• ForensiX UI => http://localhost:3000
• ForensiX Server => http://localhost:3001
• MongoDB => http://localhost:27017

HTTPS/SSL

If you want to use HTTPS for communication between on UI or Server side, place key and certificate into /certificates directory in either /server or /client directory.

To generate self-signed keys:

openssl req -nodes -new -x509 -keyout server.key -out server.cert

Change baseURL protocol to https in /client/src/axios-api.js, then rebuild the specific changed image:

docker-compose build <client|server>