Tor Crawler Tor Crawler is a robust and feature-rich web crawler designed for research purposes. It efficiently crawls .onion websites, providing advanced data processing, caching, and export functionalities. This crawler is built with scalability, distributed crawling, and advanced data processing in mind.

Features Distributed Crawling: Utilize multiple instances for distributed crawling. Caching: Redis-based caching to avoid redundant crawling. NLP Processing: Extract insights using NLTK. Image and File Handling: Download and process images and files from crawled pages. API Integration: Enrich data using external APIs. Authentication Handling: Support for sites requiring authentication. Robust Error Handling: Detailed error logging and retry mechanisms. Analytics Dashboard: Advanced analytics and visualization of crawled data. Data Anonymization: Ensure privacy by anonymizing sensitive information. Security Features: Implement security checks to avoid potential vulnerabilities. Modular Design: Facilitates the addition of new features. Advanced Scheduling: Schedule crawling jobs with advanced scheduling capabilities. Machine Learning: Integrate machine learning models for content classification. Content Filtering: Crawl specific types of content. Multi-language Support: Handle multilingual content. Notifications: Slack notifications for important events. Rate Limiting: Sophisticated rate limiting and adaptive delays. Data Export: Export data in multiple formats (CSV, JSON, XML, Parquet). Configurable Pipelines: Customizable data processing pipelines. Test Coverage: Unit tests and integration tests for robustness. Project Structure

tor_crawler/ ├── crawler.py ├── config.yaml ├── .env ├── db_setup.py ├── requirements.txt ├── web_interface.py ├── cache.py ├── nlp_processor.py ├── file_handler.py ├── api_integrations.py ├── auth_handler.py ├── notifications.py ├── scheduler.py └── README.md Installation Clone the repository:

git clone https://github.com/yourusername/tor_crawler.git cd tor_crawler Create and activate a virtual environment:

python -m venv venv source venv/bin/activate # On Windows use venv\Scripts\activate Install dependencies:

pip install -r requirements.txt Setup environment variables:

Create a .env file in the root directory with the following content:

TOR_PASSWORD=your_password DATABASE_URL=postgresql://user:password@localhost:5432/crawler_db WEB_HOST=127.0.0.1 WEB_PORT=5000 REDIS_URL=redis://localhost:6379/0 SLACK_TOKEN=your_slack_token Setup database:

Ensure you have PostgreSQL installed and running. Create a database and update the DATABASE_URL in the .env file accordingly.

Run the following script to set up the database schema:

python db_setup.py Start Tor:

Make sure Tor is installed and running:

tor Usage Start the Crawler:

python crawler.py Start the Web Interface:

python web_interface.py Monitor and Manage Crawling:

Access the web interface at http://127.0.0.1:5000 to monitor and manage the crawler.

Configuration Edit the config.yaml file to customize the crawler settings:

tor: renewal_interval: 600 # IP renewal interval in seconds

request: timeout: 30 # Request timeout in seconds delay: 5 # Delay between requests in seconds rate_limit: 10 # Maximum number of requests per minute

crawler: num_threads: 10 # Number of concurrent threads max_depth: 3 # Maximum depth for crawling cache_enabled: true # Enable or disable caching auth_required: false # Enable or disable authentication

start_urls:

• 'http://exampleonion.onion' # Add your initial .onion URLs here

data_export: formats: ['csv', 'json', 'xml', 'parquet'] # Data export formats

web: host: '127.0.0.1' port: 5000

notifications: enabled: true # Enable or disable notifications slack_channel: '#crawler-alerts' # Slack channel for notifications Advanced Features Caching The crawler uses Redis for caching. Ensure Redis is installed and running. Update the REDIS_URL in the .env file accordingly.

NLP Processing The nlp_processor.py module uses NLTK to process text. Ensure NLTK data is downloaded:

import nltk nltk.download('punkt') nltk.download('stopwords') Notifications Enable Slack notifications by setting the SLACK_TOKEN and slack_channel in the .env and config.yaml files.

Scheduling Use the scheduler.py module for advanced scheduling of crawling jobs:

import schedule import time

def job(): print("Running scheduled job...") # Add your scheduled job logic here

schedule.every().day.at("01:00").do(job)

while True: schedule.run_pending() time.sleep(1) Contributing Contributions are welcome! Please create a pull request with your enhancements.

License This project is licensed under the MIT License.