🛡️ Web Application Firewall (WAF) Simulation

Monitors, Detects, and Blocks SQL Injection and XSS attacks in real time.

Developed By: © Syed Shaheer Hussain

📘 Description

This project is a fully functional Web Application Firewall (WAF) Simulation built using Python and FastAPI, designed for educational, demonstration, and learning purposes. It simulates how a real-world WAF protects web applications from common web attacks such as SQL Injection (SQLi) and Cross-Site Scripting (XSS).

The system acts as a middleware layer between users and a protected web application, inspecting every incoming request, detecting malicious payloads, blocking attacks, and visualizing traffic statistics through a modern web dashboard.

📷 Screenshots

🎯 Objectives

1. Understand how a WAF works internally
2. Detect and block SQL Injection & XSS attacks
3. Visualize allowed vs blocked traffic
4. Learn middleware-based security enforcement
5. Simulate real-world web security behavior
6. Provide an FYP / cybersecurity demo-ready project

🧠 Concepts Covered

• Web Security
• Web Application Firewall (WAF)
• SQL Injection (SQLi)
• Cross-Site Scripting (XSS)
• HTTP Middleware
• Pattern Matching (Regex)
• Traffic Monitoring
• Secure Coding Practices
• Cybersecurity Defense Simulation

❓ What is a WAF?

A Web Application Firewall (WAF) is a security layer that:

• Monitors HTTP/HTTPS traffic
• Filters malicious requests
• Blocks web-based attacks
• Protects backend applications and databases

🔐 WAF sits between the client (browser) and the server (application).

💡 Value of This Project

• Helps students understand real-world web security
• Demonstrates attack detection logic
• Ideal for Final Year Projects (FYP)
• Practical cybersecurity learning
• Extendable to ML-based detection

🏗️ Architecture

🔄 Request Flow

1. User sends request from browser
2. Request passes through WAF middleware
3. Payload extracted (query + body)
4. SQLi/XSS rules applied
5. Decision made (Allow / Block)
6. Stats updated
7. Dashboard updated

📊 Flow Chart (Textual)

Browser Request
      ↓
WAF Middleware
      ↓
Payload Normalization
      ↓
Rule Engine (SQLi / XSS)
      ↓
Decision
  ↓        ↓
Allow     Block
  ↓        ↓
App     Error Page

📂 Folder Structure

WAF-Project/
│
├── main.py              # FastAPI app & middleware
├── waf_engine.py        # Detection logic
├── rules.py             # SQLi & XSS regex patterns
├── logger.py            # Attack logging
├── templates/
│   ├── login.html
│   └── dashboard.html
├── static/
│   └── style.css
├── logs/
│   └── attacks.log
├── requirements.txt
└── README.md

🛠️ Technologies Used

• 🐍 Python 3.10+
• ⚡ FastAPI
• 🌐 HTML5, CSS3, JavaScript
• 📊 Chart.js
• 🔍 Regex-based Detection
• 🧪 REST APIs

🧪 Features

1. ✅ SQL Injection detection
2. ✅ XSS attack detection
3. 📊 Live traffic visualization
4. 🚫 Automatic request blocking
5. 📈 Allowed vs Blocked graph
6. 🧾 Attack logging
7. 🧪 Manual payload testing
8. 🎓 Educational dashboard

⚙️ Functions & Modules

🔹 detect_attack(payload)

• Scans payload for SQLi/XSS
• Uses regex rules
• Returns verdict

🔹 Middleware

• Intercepts every HTTP request
• Applies WAF logic
• Blocks malicious traffic

🔹 /test Endpoint

• Manual testing of payloads
• Simulates attacks

🔹 /stats Endpoint

• Returns allowed/blocked counts

🧑‍💻 Installation (Step-by-Step)

1️⃣ Install Python

Download Python from:
https://www.python.org

✔ Make sure Add Python to PATH is checked

2️⃣ Install Dependencies

pip install fastapi uvicorn jinja2

3️⃣ Project Setup

cd waf_simulation

▶️ How to Run

uvicorn main:app --reload

Expected output:

Running on http://127.0.0.1:8000

🌐 How to Open (Chrome)

1. Open Google Chrome
2. Go to:

http://127.0.0.1:8000

🔐 Login Details

Username: admin
Password: admin123

🧪 How to Use

1. Login to dashboard
2. Enter payload in text box
3. Click Detect
4. View result (Allowed / Blocked)
5. Watch graph update automatically

💥 Example Payloads

❌ SQL Injection

' OR 1=1 --

❌ XSS

<script>alert(1)</script>

✅ Normal Input

hello world

⚠️ Cautions

📌 Important Notes

• Designed for learning & demo purposes
• Easily extendable
• Clean & modular code

📢 Disclaimer

🚀 Future Enhancements

1. 🤖 Machine Learning-based detection
2. 🔐 Role-based access control
3. 🌍 IP reputation system
4. 📄 PDF attack reports
5. ⏱️ Time-series traffic graphs
6. 🧪 Automated attack generator

🏁 Conclusion

This WAF Simulation provides a clear, realistic, and interactive understanding of how modern web security systems defend against common attacks. It bridges the gap between theory and practical cybersecurity implementation.

⭐ Support & Engagement

If you find this repository useful or insightful, please consider:

• ⭐ Starring the repository
• 🔁 Sharing it within your network
• 👤 Following my GitHub profile for future projects and updates

Your support helps drive continued innovation and open-source contributions.

— Syed Shaheer Hussain

© Copyright

© 2026 Syed Shaheer Hussain

All rights reserved.