🚨 CryBIT - AI-Powered Real-Time Scam Detection System

CryBIT is an advanced AI-powered scam detection system that monitors Telegram channels in real-time to detect and flag cryptocurrency scams, phishing attempts, blacklisted wallets, and fraudulent messages using ML, NLP, and rule-based heuristics.

🔧 Tech Stack

• Backend: Flask (Python)
• Telegram Integration: Telethon
• Database: MongoDB
• AI Modules: Scikit-learn, Transformers, EasyOCR
• Frontend: HTML, AJAX (for dynamic UI)

⚙️ Features

• Real-time message monitoring via Telethon
• Multi-layered scam detection (ML, NLP, OCR, URL & Wallet checks)
• Cumulative risk scoring system
• Dashboard UI to manage channels and view flagged messages
• Telegram alerts for high-risk scams
• Modular architecture for scalability and maintenance

🛠️ Setup Instructions

1. 📦 Prerequisites

Ensure you have the following installed:

• Python 3.8+
• MongoDB (running locally or via Atlas)
• pip (Python package manager)

2. 🔑 Configuration

Create or update the config.json file with the following structure:

{
  "api_keys": {
    "google_safe_browsing": "YOUR_API_KEY",
    "phish_tank": "YOUR_API_KEY"
  },
  "telegram": {
    "api_id": "YOUR_API_ID",
    "api_hash": "YOUR_API_HASH",
    "bot_token": "YOUR_BOT_TOKEN",
    "admin_id": "YOUR_TELEGRAM_ID"
  },
  "scam_keywords": ["free bitcoin", "investment", "double your money"],
  "risk_threshold": 0.4
}

3. 📥 Install Dependencies

pip install -r requirements.txt

If requirements.txt is missing, install dependencies manually:

pip install flask telethon pymongo scikit-learn sentence-transformers easyocr

🚀 Running the App

Use a split terminal or two terminal tabs:

Terminal 1: Start Flask Backend

python main.py

Terminal 2: Start Telegram Monitoring

python telethon_integration.py

Open your browser and visit: http://localhost:5000

📊 Risk Scoring Breakdown

Detection Technique	Weight Added
ML Model Prediction	+0.6
Keyword Match	+0.5
Phishing URL Detected	+0.6
Crypto Wallet Blacklist Match	+0.7
NLP Embedding Match (Semantic)	+0.2
OCR-based Scam Text Detection	+0.3

Messages are flagged if the total risk score > 0.4 (default threshold).

🙌 Contribution Guidelines

🔧 Want to Contribute?

We welcome contributions from developers!

📌 Here's how you can help:

• Improve scam detection accuracy (enhance ML models or NLP logic)
• Add support for more scam patterns or keywords
• Refactor code for modularity and scalability
• UI/UX improvements for the dashboard
• Write documentation or create test cases

🛠️ To Contribute:

1. Fork this repository
2. Create a new branch (feature/your-feature-name)
3. Commit your changes with clear messages
4. Push to your branch and create a Pull Request

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🧠 Future Enhancements

• Admin login & access control
• Visual scam heatmaps & statistics
• PDF export of scam reports
• Multilingual support
• Integration with Discord, WhatsApp, etc.

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📬 Contact

For support, questions, or collaboration:

📧 Email: [mukeshkumar.cse24@gmail.com]

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📄 License

This project is licensed under the MIT License.