Vanguard - AI-Powered Phishing Detection System

Overview

Vanguard is an AI-powered phishing detection system designed to identify and mitigate phishing attacks. It utilizes machine learning to analyze email content, URLs, and website data, offering real-time threat intelligence and scalable security solutions for organizations and individuals.

Features

• Phishing Email Detection: Analyzes email content, sender reputation, and embedded URLs.
• Malicious Website Detection: Identifies fraudulent websites using advanced content and URL analysis.
• Real-Time Threat Intelligence: Leverages threat intelligence feeds and domain reputation databases.
• Scalability: Efficiently processes large volumes of emails and websites.
• Seamless Integration: Compatible with email security tools, SIEMs, and web security platforms.

Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)

Steps

1. Clone the repository:

   git clone https://github.com/your-username/vanguard.git
   cd phishing-detection-system/ 

2. Install dependencies:

   pip install -r requirements.txt

3. Set up the environment:
   • Create a .env file in the root directory and add your API keys and configurations:

     THREAT_INTEL_API_KEY=your_api_key
     EMAIL_API_KEY=your_email_api_key
     

4. Run the API:

   cd src/api
   uvicorn main:app --reload

Usage

API Endpoints

• Detect Phishing in a Single Email:

  curl -X POST "http://127.0.0.1:8000/detect-phishing" \
  -H "Content-Type: application/json" \
  -d '{"content": "Dear user, please click this link: http://malicious-site.com"}'

• Detect Phishing in a Batch of Emails:

  curl -X POST "http://127.0.0.1:8000/detect-phishing-batch" \
  -H "Content-Type: application/json" \
  -d '{"emails": ["Email 1 content", "Email 2 content"]}'

• Health Check:

  curl "http://127.0.0.1:8000/health"

Training the Model

To train the phishing detection model:

python src/models/train.py

Updating Threat Intelligence

To update the blacklist:

python src/threat_intelligence/blacklist_updater.py

Evaluation Metrics

• Detection Accuracy:
  • Precision: 0.94
  • Recall: 0.96
  • F1 Score: 0.95
  • AUC-ROC: 0.98
• False Positive Rate: 2%
• False Negative Rate: 1%

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Open a pull request.

Contact

For questions or feedback, please contact:

• Runtime Terrors: devayanee999@gmail.com