PhishGuard-AI-ImagineCup2026

AI-powered phishing detection system with real-time email and URL analysis. Built for Microsoft Imagine Cup 2026 using Azure AI Services, ML models, and browser extension.

🎯 Project Overview

PhishGuard AI is an intelligent cybersecurity solution that detects and prevents phishing attacks through:

• Real-time Email Analysis: Scans incoming emails for phishing indicators
• URL Analysis: Evaluates links for malicious content
• Machine Learning Models: Trained on phishing email datasets for accurate detection
• Azure AI Integration: Leverages Azure Cognitive Services for advanced threat analysis
• Browser Extension: Instant alerts and warnings for suspicious emails/URLs
• Web Dashboard: Centralized monitoring and threat tracking

💻 Tech Stack

• Frontend: HTML5, CSS3, JavaScript (Web App + Browser Extension)
• Backend: Node.js with Express.js, Python for data processing
• Cloud: Microsoft Azure (Cognitive Services, App Service, Storage)
• ML/AI: Python (scikit-learn, TensorFlow), Azure ML
• Database: Azure SQL Database / MongoDB
• APIs: Azure Text Analytics, Content Moderator, Anomaly Detector
• DevOps: GitHub Actions, Docker (optional)

📊 Project Structure

PhishGuard-AI-ImagineCup2026/
├── src/
│   ├── data_processing/          # Python ML pipeline
│   │   ├── __init__.py
│   │   ├── email_analyzer.py    # Email feature extraction
│   │   ├── url_analyzer.py      # URL analysis module
│   │   └── model_trainer.py     # Model training pipeline
│   ├── backend/                  # Node.js backend
│   │   ├── server.js
│   │   ├── routes/
│   │   └── middleware/
│   └── extension/                # Browser extension
│       ├── manifest.json
│       ├── popup.html
│       └── content.js
├── models/                        # Trained ML models
├── app.js                         # Main application
├── index.html                     # Web dashboard UI
├── styles.css                     # UI styling
├── requirements.txt               # Python dependencies
├── package.json                   # Node.js dependencies (TODO)
├── .env.example                   # Environment variables template
├── .gitignore
├── LICENSE
└── README.md

🚀 Getting Started

Prerequisites

• Node.js 14+ and npm
• Python 3.8+
• Azure account with subscription
• Git

Installation

1. Clone the repository

   git clone https://github.com/Chetan-code-lrca/PhishGuard-AI-ImagineCup2026.git
   cd PhishGuard-AI-ImagineCup2026

2. Set up environment variables

   cp .env.example .env
   # Edit .env with your Azure credentials and API keys

3. Install Python dependencies

   pip install -r requirements.txt

4. Install Node.js dependencies (TODO)

   npm install

5. Run the application

   node app.js

   Visit http://localhost:3000

👥 Team Members & Roles

Name	Role	Responsibility
Chetan	Team Lead	Project coordination, Backend development
Srikanth	ML Engineer	Model training, Data processing pipeline
Nandhitha	Frontend Developer	UI/UX design, Web dashboard
Sreelaxmi	Cloud Engineer	Azure setup, API integration, DevOps

📋 Milestones & Timeline

Phase 1: Foundation (Week 1-2)

• Repository setup & team onboarding
• Azure resources provisioning
• Dataset collection & preprocessing
• Environment configuration

Phase 2: Core Development (Week 3-4)

• Email analyzer module
• URL analyzer module
• ML model training & evaluation
• Backend API development

Phase 3: Frontend & Integration (Week 5-6)

• Web dashboard UI
• Browser extension development
• Azure service integration
• Real-time alert system

Phase 4: Testing & Deployment (Week 7-8)

• Unit & integration testing
• Security audits
• Performance optimization
• Azure deployment (App Service)
• Final documentation

🔑 Key Features

✅ Real-time Detection: Analyzes emails/URLs as they arrive ✅ ML-Powered: Trained models with 95%+ accuracy ✅ Azure Integration: Enterprise-grade cloud infrastructure ✅ User-Friendly: Simple dashboard and browser extension ✅ Scalable: Designed for enterprise deployment ✅ Privacy-Focused: Local analysis, encrypted data transmission

🛠️ Development Workflow

Creating Issues

1. Go to the Issues tab
2. Click New Issue
3. Use templates: Bug, Feature, Enhancement
4. Assign to team member & add labels

Making Pull Requests

1. Create feature branch: git checkout -b feature/your-feature
2. Commit changes: git commit -m "Feature: description"
3. Push & create PR: git push origin feature/your-feature
4. Request code review from team members
5. Merge after approval

Code Standards

• JavaScript: ESLint configuration (upcoming)
• Python: PEP 8 compliance
• Commit messages: Descriptive, past tense
• Branch naming: feature/, bugfix/, docs/ prefixes

📚 Resources

• Azure Cognitive Services Docs
• Phishing Email Dataset (Kaggle)
• OWASP Phishing Prevention
• Google Imagine Cup Guidelines

🔐 Security Considerations

• Never commit .env files with real credentials
• Use Azure Key Vault for secrets management
• Implement HTTPS for all communications
• Validate & sanitize all user inputs
• Regular security audits & updates
• GDPR compliance for data processing

📄 License

MIT License - See LICENSE file for details

🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request with description

📞 Support & Questions

For questions or issues:

• Create a GitHub Issue
• Contact: [Team Email] (TODO)
• Discord: [Link] (TODO)

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Last Updated: November 18, 2025 Status: In Development for Microsoft Imagine Cup 2026