🛡️ SQL Injection Attack Detection System

Machine Learning-Based Security System for SQL Injection Prevention

Detect and prevent SQL injection attacks using pattern analysis and machine learning

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📋 Table of Contents

• Overview
• Features
• Attack Demonstrations
• How It Works
• Tech Stack
• Installation
• Usage
• Detection Techniques
• Model Performance
• Security Best Practices
• Project Structure
• Contributing
• License

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🎯 Overview

A comprehensive security-focused project demonstrating:

1. ⚠️ Real-world SQL injection vulnerabilities and attack vectors
2. 🛡️ Machine learning-based detection using pattern analysis
3. ✅ Secure coding practices and mitigation strategies
4. 📚 Educational demonstrations for cybersecurity learning

Why This Matters

SQL Injection remains in the OWASP Top 10 web application security risks. This project provides:

• 🎓 Educational value - Learn attack patterns and defenses
• 🔍 Detection capabilities - ML-powered threat identification
• 💡 Best practices - Secure query implementation examples
• 🛠️ Practical tools - Ready-to-use detection system

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✨ Features

🔴 Vulnerability Demonstrations

• ✅ Authentication bypass examples
• ✅ Union-based SQL injection
• ✅ Blind SQL injection techniques
• ✅ Time-based SQL injection
• ✅ Error-based exploitation

🟢 Detection & Prevention

• ✅ Pattern-based detection engine
• ✅ SVM classifier for query analysis
• ✅ Real-time threat assessment
• ✅ Confidence scoring system
• ✅ Query sanitization examples
• ✅ Prepared statement demonstrations

📊 Analysis Tools

• ✅ Query pattern visualization
• ✅ Attack vector identification
• ✅ Feature extraction pipeline
• ✅ Performance metrics dashboard

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⚠️ Attack Demonstrations

⚠️ WARNING: These examples are for educational purposes only. Never use on systems without authorization.

1. Authentication Bypass

Vulnerable Code:

# ❌ VULNERABLE - Never do this!
username = request.form['username']
password = request.form['password']

query = f"SELECT * FROM users WHERE username='{username}' AND password='{password}'"
cursor.execute(query)

Attack:

Username: admin' OR '1'='1
Password: anything

Resulting Query:
SELECT * FROM users WHERE username='admin' OR '1'='1' AND password='anything'
-- This always returns true!

Secure Alternative:

# ✅ SECURE - Use parameterized queries
query = "SELECT * FROM users WHERE username=%s AND password=%s"
cursor.execute(query, (username, password))

2. Union-Based Injection

Attack:

' UNION SELECT username, password FROM admin_users--

Extracting data from other tables

3. Blind SQL Injection

Attack:

' AND 1=1--  # Returns true
' AND 1=2--  # Returns false

# Extracting data one bit at a time

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🔬 How It Works

Detection Pipeline

┌─────────────┐
│ Input Query │
└──────┬──────┘
       │
       ▼
┌──────────────────┐
│ Feature          │
│ Extraction       │
│ • Keywords       │
│ • Patterns       │
│ • Structure      │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│ ML Classifier    │
│ (SVM)            │
│ • Pattern Match  │
│ • Anomaly Score  │
└────────┬─────────┘
         │
         ▼
┌──────────────────┐
│ Threat           │
│ Assessment       │
│ • Safe/Malicious │
│ • Confidence %   │
│ • Attack Type    │
└──────────────────┘

Feature Extraction

def extract_features(query):
    features = {
        # Suspicious keywords
        'has_or': 'OR' in query.upper(),
        'has_union': 'UNION' in query.upper(),
        'has_comment': '--' in query or '/*' in query,
        'has_semicolon': ';' in query,
        
        # Pattern analysis
        'num_quotes': query.count("'") + query.count('"'),
        'num_equals': query.count('='),
        'num_dashes': query.count('-'),
        
        # Structure analysis
        'query_length': len(query),
        'has_always_true': check_tautology(query),
        'suspicious_encoding': check_encoding(query),
        
        # Advanced patterns
        'has_sleep': 'SLEEP' in query.upper(),
        'has_benchmark': 'BENCHMARK' in query.upper(),
        'has_information_schema': 'INFORMATION_SCHEMA' in query.upper()
    }
    return features

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🛠️ Tech Stack

Core Technologies

Additional Tools

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📥 Installation

Prerequisites

• Python 3.8+
• MySQL Server 8.0+
• Basic understanding of SQL

Setup

# 1. Clone repository
git clone https://github.com/ares-coding/sql-injection-attack-detection.git
cd sql-injection-attack-detection

# 2. Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

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

# 4. Setup MySQL database
mysql -u root -p < database/setup.sql

# 5. Configure database connection
cp config.example.py config.py
# Edit config.py with your MySQL credentials

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🚀 Usage

1. Train the Detection Model

# Train SVM classifier on labeled SQL queries
python train_model.py --dataset data/sql_queries.csv --output models/detector.pkl

# Output:
# Training accuracy: 96.8%
# Test accuracy: 94.2%
# Model saved to models/detector.pkl

2. Test Vulnerability Demonstrations

# Run vulnerable application (for educational purposes)
python vulnerable_app.py

# Test SQL injection attacks
python test_attacks.py

3. Use the Detection System

from sql_detector import SQLInjectionDetector

# Initialize detector
detector = SQLInjectionDetector(model_path='models/detector.pkl')

# Analyze a query
query = "SELECT * FROM users WHERE id = '1' OR '1'='1'"
result = detector.predict(query)

print(f"Is Malicious: {result['is_malicious']}")
print(f"Confidence: {result['confidence']:.2%}")
print(f"Attack Type: {result['attack_type']}")
print(f"Risk Level: {result['risk_level']}")

Output:

Is Malicious: True
Confidence: 98.7%
Attack Type: Authentication Bypass (Tautology)
Risk Level: CRITICAL

4. Run the Web Interface

# Start Flask application
python app.py

# Visit http://localhost:5000

5. Secure Implementation Examples

# Example 1: Parameterized Queries
def get_user_safe(user_id):
    query = "SELECT * FROM users WHERE id = %s"
    cursor.execute(query, (user_id,))
    return cursor.fetchone()

# Example 2: Input Validation
def validate_input(user_input):
    # Whitelist allowed characters
    allowed = set("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_-")
    if not set(user_input).issubset(allowed):
        raise ValueError("Invalid characters detected")
    return user_input

# Example 3: ORM Usage (SQLAlchemy)
from sqlalchemy import select
stmt = select(User).where(User.id == user_id)
result = session.execute(stmt)

---

🔍 Detection Techniques

Pattern-Based Detection

INJECTION_PATTERNS = [
    r"(\bOR\b|\bAND\b)\s+['\"]?\w+['\"]?\s*=\s*['\"]?\w+['\"]?",  # Tautology
    r"\bUNION\b.*\bSELECT\b",                                      # Union injection
    r";\s*DROP\b",                                                 # Drop table
    r"--",                                                         # SQL comments
    r"/\*.*\*/",                                                   # Block comments
    r"\bEXEC\b|\bEXECUTE\b",                                      # Command execution
    r"\bSLEEP\b\s*\(",                                            # Time-based
    r"\bBENCHMARK\b\s*\(",                                        # Performance-based
    r"information_schema",                                         # Schema extraction
]

Machine Learning Features

Feature Category	Examples
Keyword-based	OR, UNION, DROP, EXEC, SLEEP
Symbol-based	Quotes, dashes, semicolons, comments
Structural	Query length, nesting level, clause count
Semantic	Tautologies, always-true conditions
Encoding	Hex encoding, URL encoding, Unicode

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📊 Model Performance

Classification Results

Metric	Value
Accuracy	94.2%
Precision	93.8%
Recall	94.7%
F1-Score	94.2%
False Positive Rate	3.2%
False Negative Rate	2.8%

Confusion Matrix

                Predicted
              Benign  Malicious
Actual Benign   1,842      58
     Malicious    52   2,048

Attack Type Detection

Attack Type	Detection Rate
Tautology (OR 1=1)	98.5%
Union-based	96.3%
Blind SQL	92.7%
Time-based	94.1%
Comment-based	97.8%

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🔐 Security Best Practices

✅ DO's

1. Use Parameterized Queries

   cursor.execute("SELECT * FROM users WHERE id = %s", (user_id,))

2. Use ORM Frameworks

   User.query.filter_by(id=user_id).first()

3. Input Validation

   if not user_input.isalnum():
       raise ValueError("Invalid input")

4. Principle of Least Privilege

   GRANT SELECT ON database.users TO 'app_user'@'localhost';

5. Escape Special Characters

   escaped = db.escape_string(user_input)

❌ DON'Ts

1. Never concatenate user input

   # ❌ BAD
   query = f"SELECT * FROM users WHERE id = '{user_id}'"

2. Don't trust any user input

   # ❌ BAD - Even "safe-looking" input can be malicious

3. Don't show detailed errors to users

   # ❌ BAD - Reveals database structure
   except Exception as e:
       return str(e)

4. Don't use dynamic table/column names from user input

   # ❌ BAD
   query = f"SELECT * FROM {user_table}"

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📁 Project Structure

sql-injection-detection/
├── 📁 data/
│   ├── sql_queries.csv          # Training dataset
│   ├── attack_samples.txt       # Attack examples
│   └── benign_samples.txt       # Legitimate queries
├── 📁 database/
│   ├── setup.sql                # Database schema
│   ├── sample_data.sql          # Test data
│   └── vulnerable_tables.sql    # Demo vulnerable setup
├── 📁 models/
│   ├── svm_detector.pkl         # Trained SVM model
│   └── feature_extractor.pkl    # Feature scaler
├── 📁 src/
│   ├── feature_extraction.py    # Feature engineering
│   ├── pattern_detection.py     # Regex-based detection
│   ├── ml_classifier.py         # ML model training
│   └── utils.py                 # Utility functions
├── 📁 demos/
│   ├── vulnerable_app.py        # Intentionally vulnerable app
│   ├── secure_app.py            # Secured version
│   └── attack_scenarios.py      # Attack demonstrations
├── 📁 notebooks/
│   ├── 01_data_analysis.ipynb
│   ├── 02_pattern_study.ipynb
│   └── 03_model_evaluation.ipynb
├── 📁 web/
│   ├── app.py                   # Flask web interface
│   ├── templates/
│   └── static/
├── train_model.py               # Model training script
├── test_detector.py             # Testing script
├── requirements.txt
└── README.md

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🧪 Testing

Run Unit Tests

# Run all tests
python -m pytest tests/

# Run specific test suite
python -m pytest tests/test_detection.py -v

# Run with coverage
python -m pytest --cov=src tests/

Test Attack Scenarios

# Test all attack types
python test_attacks.py --all

# Test specific attack
python test_attacks.py --type tautology

# Generate test report
python test_attacks.py --report output/test_results.html

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📚 Educational Resources

Learning Path

1. Understand SQL Basics → W3Schools SQL Tutorial
2. Learn SQL Injection → OWASP SQL Injection Guide
3. Practice Safely → WebGoat, DVWA
4. Study This Project → Run demos, read code
5. Build Defenses → Implement secure code

Recommended Reading

• 📖 OWASP Top 10
• 📖 SQL Injection Cheat Sheet
• 📖 Parameterized Queries Guide

Practice Platforms

• 🎯 HackTheBox
• 🎯 TryHackMe
• 🎯 PentesterLab

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⚖️ Legal & Ethical Notice

⚠️ IMPORTANT DISCLAIMER

This project is for educational and research purposes only.

• ✅ Use on systems you own or have explicit permission to test
• ❌ Never use on production systems without authorization
• ❌ Unauthorized access to computer systems is illegal

The author assumes no liability for misuse of this software.

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🤝 Contributing

We welcome contributions! Please follow these guidelines:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Add tests for new functionality
4. Ensure all tests pass (pytest)
5. Commit changes (git commit -m 'Add AmazingFeature')
6. Push to branch (git push origin feature/AmazingFeature)
7. Open a Pull Request

Contribution Ideas

• 🐛 Bug fixes and improvements
• 📝 Documentation enhancements
• 🧪 Additional test cases
• 🎨 Web interface improvements
• 🔍 New detection patterns
• 🌐 Multi-language support

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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👤 Author

Au Amores - Full Stack Developer & Cybersecurity Enthusiast

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🙏 Acknowledgments

• OWASP Foundation for security resources
• MySQL documentation and community
• Scikit-learn contributors
• Security researchers and ethical hackers

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📚 Citation

@software{sql_injection_detection,
  author = {Amores, Au},
  title = {SQL Injection Attack Detection using Machine Learning},
  year = {2025},
  url = {https://github.com/ares-coding/sql-injection-attack-detection}
}

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⭐ Star this repo if it helped you learn about SQL injection!

🔒 Stay secure, code safely!

Made with 🛡️ and ☕ by Ares