🛡️ PhishShield - Advanced Spam & Fraud Detection System

A sophisticated AI-powered system for detecting spam messages, financial fraud, and phishing attempts in real-time

Designed to combat modern scams including Jamtara-style financial fraud schemes

🚀 Quick Start • 📊 Features • 🛠️ Installation • 📱 Usage • 📈 Roadmap

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🌟 Overview

PhishShield is a comprehensive fraud detection system that combines machine learning with rule-based detection to identify harmful messages with high accuracy. The system specifically targets modern fraud schemes like those seen in Jamtara-style scams, providing real-time protection against:

• 💳 Financial Fraud (Banking, UPI, loan scams)
• 🎣 Phishing Attempts (Credential theft, fake offers)
• 📧 Traditional Spam (Unwanted marketing, malicious content)
• 📱 SMS Fraud (OTP theft, fake alerts)

📊 Features

🧠 Hybrid AI Detection

• Neural Network: 97% accuracy on spam detection
• Rule-Based Engine: 30+ financial fraud patterns
• Pattern Recognition: URLs, money amounts, fraud keywords
• Real-Time Analysis: Instant fraud scoring

🎯 Advanced Detection Capabilities

Feature	Description	Coverage
💰 Financial Keywords	Banking, UPI, card-related terms	25+ patterns
🎣 Phishing Indicators	Urgency, fake offers, social engineering	20+ patterns
🔗 URL Detection	Malicious domains, shortened links	7+ patterns
⏰ Time Pressure	Urgency manipulation tactics	Real-time detection
💸 Money Mentions	Currency amounts in messages	Multi-currency

🌐 User Interface

• Web-Based Dashboard: Streamlit-powered interface
• Binary Classification: Simple SPAM vs LEGITIMATE results
• Detailed Analysis: Risk breakdown and explanations
• Example Library: Pre-loaded test cases
• Safety Guidelines: Built-in fraud protection tips

🛠️ Installation

📋 Prerequisites

• Python 3.7+ (Recommended: Python 3.9+)
• pip package manager
• Git (for cloning the repository)

🚀 Quick Setup

Method 1: Automated Installation (Recommended)

# Clone the repository
git clone https://github.com/divinixx/PhishShield.git
cd PhishShield

# Run automated setup (Windows)
run_phishshield.bat

# Or use Python setup script
python setup_and_run.py

Method 2: Manual Installation

# 1. Clone the repository
git clone https://github.com/divinixx/PhishShield.git
cd PhishShield

# 2. Install dependencies
pip install -r requirements.txt

# 3. Train the model (if not already trained)
python spam.py

# 4. Launch the application
streamlit run app.py

📦 Required Libraries

streamlit>=1.25.0
torch>=2.0.1
scikit-learn>=1.3.0
nltk>=3.8.1
pandas>=2.0.3
numpy>=1.24.3
joblib>=1.3.2

📱 Usage

🖥️ Web Interface

1. Start the application:

   streamlit run app.py

2. Open your browser to http://localhost:8501

3. Analyze messages:

   • Enter any message in the text area
   • Click "🔍 Analyze Message"
   • View binary classification result
   • Check detailed fraud analysis

🧪 Testing & Validation

# Test fraud detection with sample messages
python test_fraud_detection.py

# Verify system components
python -c "from app import load_model_components; print('✅ All components loaded successfully!')"

📊 Example Results

❌ SPAM Detection

Input: "Dear customer, your debit card will be blocked within 2 hrs. Call 9876543210 to reactivate."

Output:
🚨 SPAM DETECTED (90% confidence)
📊 Fraud Risk Score: 100%
💳 Financial Keywords: debit card, reactivate
� Suspicious Content: Phone number detected
⏰ Time Pressure: within 2 hrs

✅ LEGITIMATE Detection

Input: "Hi! Are we still meeting for lunch tomorrow at 12pm?"

Output:
✅ LEGITIMATE MESSAGE (95% confidence)
📊 Fraud Risk Score: 0%
ℹ️ No suspicious patterns detected

🎯 Detection Examples

🚨 Financial Fraud Examples

Message Type	Example	Detection
Banking Fraud	"Your account is blocked. Call 9876543210 immediately!"	✅ SPAM
Loan Scam	"Pre-approved loan of ₹5,00,000. Apply: http://scam-site.com"	✅ SPAM
Prize Scam	"You won iPhone! Pay ₹99 shipping: http://fake-apple.com"	✅ SPAM
OTP Theft	"Share OTP 123456 to verify your account immediately"	✅ SPAM

✅ Legitimate Message Examples

Message Type	Example	Detection
Personal	"Hi! Are we meeting for lunch tomorrow?"	✅ LEGITIMATE
Business	"Team meeting at 3 PM in conference room B"	✅ LEGITIMATE
Appointments	"Doctor visit reminder: Friday at 3pm"	✅ LEGITIMATE
Delivery	"Package will be delivered between 10 AM - 2 PM"	✅ LEGITIMATE

🏗️ Architecture

🧠 Model Architecture

Input Message
     ↓
┌─────────────────┐    ┌─────────────────┐
│ Rule-Based      │    │ ML Classification│
│ Fraud Analysis  │    │ (Neural Network) │
│                 │    │                  │
│ • Financial     │    │ • TF-IDF         │
│ • Phishing      │    │ • 5000 features  │
│ • URLs          │    │ • 128→64→2       │
│ • Keywords      │    │ • PyTorch        │
└─────────────────┘    └─────────────────┘
     ↓                       ↓
┌─────────────────────────────────────────┐
│         Confidence Fusion               │
│    (Hybrid Decision Engine)             │
└─────────────────────────────────────────┘
     ↓
Final Classification: SPAM / LEGITIMATE

🔧 Technical Stack

• Backend: Python 3.9+
• ML Framework: PyTorch 2.0+
• Feature Engineering: Scikit-learn, NLTK
• Web Interface: Streamlit
• Data Processing: Pandas, NumPy
• Model Persistence: JobLib

📁 Project Structure

PhishShield/
├── 📄 app.py                    # Main Streamlit application
├── 🧠 spam.py                   # Model training script
├── 📊 spam.csv                  # Training dataset
├── 🔧 requirements.txt          # Python dependencies
├── 🚀 run_phishshield.bat      # Windows launcher
├── 🛠️ setup_and_run.py         # Automated setup script
├── 🧪 test_fraud_detection.py   # Testing utilities
├── 📚 README.md                 # Project documentation
└── 🤖 Model Files/
    ├── spam_classifier.pth      # Trained neural network
    ├── tfidf_vectorizer.pkl     # TF-IDF vectorizer
    └── label_encoder.pkl        # Label encoder

⚡ Performance Metrics

Metric	Score	Description
Overall Accuracy	97%	General spam detection accuracy
Financial Fraud Detection	95%+	Specialized fraud pattern detection
False Positive Rate	<3%	Legitimate messages marked as spam
Processing Speed	<100ms	Average analysis time per message
Model Size	~2.5MB	Compact for deployment

🔒 Security Features

🛡️ Fraud Protection

• Multi-layer Detection: ML + Rule-based validation
• Real-time Scoring: Instant risk assessment
• Pattern Recognition: Advanced fraud indicators
• Educational Alerts: Built-in safety guidelines

📋 Safety Guidelines

• ❌ Never share: PIN, OTP, passwords via SMS
• ✅ Always verify: Requests through official channels
• 🚨 Report fraud: Suspicious messages to authorities
• 🛡️ Stay informed: Keep updated on latest scam tactics

📈 Roadmap

🎯 Upcoming Features

• 📱 Mobile App: React Native mobile application
• 🌐 Multi-language: Hindi, Tamil, Telugu support
• 🔊 Voice Analysis: Audio message fraud detection
• 📞 Call Integration: Real-time call analysis
• 🤖 Advanced AI: Transformer-based models
• 📊 Analytics Dashboard: Fraud trend analysis
• 🔌 API Integration: RESTful API for third-party apps

🏢 Enterprise Features

• 👥 Team Management: Multi-user support
• 📈 Reporting: Advanced analytics and insights
• 🔒 Enterprise Security: Enhanced data protection
• ⚡ High Performance: Scalable cloud deployment

This is a personal project created by divinixx for educational and research purposes.

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⭐ Star this repository if PhishShield helped protect you from fraud! ⭐

🛡️ Protecting users from fraud, one message at a time 🛡️

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📊 Usage Analytics