sarthak4399 / darc

DARC : Dark Web Monitoring and Analysis DARC is an initiative designed to automate the exploration and examination of the dark web. By using the Scrapy framework alongside the Tor network, DARC navigates and extracts data from .onion sites efficiently and securely. This project addresses the ongoing need for systematic monitoring of the dark web

DARC

Overview

The DARC is a specialized tool designed to navigate and index content on the dark web. It operates over the Tor network to ensure anonymity and security, collecting data for analysis, research, or monitoring purposes. This crawler is built to handle the unique complexities and potential risks associated with the dark web environment.

Features

• Anonymity and Security:

  • Tor Network Compatibility: Operates over the Tor network for anonymous browsing.
  • IP Masking: Regularly changes IP addresses to avoid detection and tracking.

• Advanced Crawling Techniques:

  • Customizable Crawl Depth: Control how deeply the crawler navigates within sites.
  • Pattern Recognition: Uses machine learning algorithms to identify and prioritize relevant content.
  • Multi-threading: Handles multiple connections simultaneously for efficient data collection.

• Data Extraction and Management:

  • Content Parsing: Extracts text, images, and other media formats.
  • Structured Data Storage: Organizes collected data into databases or structured formats.
  • Metadata Collection: Gathers additional information such as timestamps, URLs, and site hierarchies.

• Safety and Compliance:

  • Ethical Crawling Guidelines: Adheres to legal and ethical standards.
  • Regular Updates: Continuously updates algorithms to adapt to the evolving dark web.
  • Alert Systems: Notifies users of potential risks or illegal content encountered.

Prerequisites

• Python 3.6+
• Tor: Ensure Tor is installed and running on your system.
• Python Packages:
  • requests
  • beautifulsoup4
  • pysocks

You can install the required Python packages using:

pip install -r requirements.txt

Credits

This project is developed and maintained by Team DARC. Special thanks to all the contributors for their efforts and dedication to making this project a success.

Team DARC

• Chanpreet Kaur
• Sahiba Sethi
• Sarthak Khandare
• Harsh Murjani

Feel free to modify the wording or add more detailed acknowledgments as necessary to suit your team's preferences.