🛡️ SQLi-IDPS: Intelligent SQL Injection Detection & Prevention System

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Welcome to the repository of my graduation project: SQLi-IDPS, an intelligent system designed to detect and prevent SQL Injection (SQLi) attacks. I developed this system to provide a robust, real-time defense layer for web applications using a Reverse Proxy architecture.

The core objective of this project was to build a security solution that sits in front of any web application, inspecting incoming traffic and blocking malicious payloads before they ever reach the backend server.

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🧠 How It Works (Hybrid Detection Methodology)

I implemented a Hybrid Detection Strategy that combines speed with high accuracy:

1. Nginx Reverse Proxy: All incoming HTTP/HTTPS requests are intercepted by Nginx. Using a custom Lua module, the request data is forwarded to my inspection service.
2. Dual-Layer Inspection:
   • Signature-based Detection: Utilizing the libinjection library for lightning-fast detection of known SQLi patterns.
   • Machine Learning-based Detection: Leveraging a trained Support Vector Machine (SVM) model to identify complex, obfuscated, or zero-day attacks that traditional signatures might miss.
3. Real-time Response: If an attack is detected, the request is blocked immediately with a 403 Forbidden response. The incident is logged, and an instant alert is sent to the administrator via Email/SMS.

System Architecture

The following diagram illustrates the data flow and how the components interact:

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💻 Technologies & Tools

I chose a powerful stack to ensure reliability and performance:

Component	Technology	My Role
Backend Service	Python + Flask	Developed the inspection engine, RESTful APIs, and management logic.
Web Server/Proxy	Nginx + Lua	Configured the reverse proxy and integrated it with the Python backend.
AI/Machine Learning	Scikit-learn (SVM)	Trained and optimized the ML model for high-precision detection.
Frontend Dashboard	React.js (Integrated)	Designed a modern UI for monitoring logs, alerts, and system status.
Database	SQLite / SQLAlchemy	Managed local storage for incident logs and system configurations.

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📸 System Screenshots

Here is a look at the final product in action:

Feature	Screenshot
Login Interface
Main Dashboard
Detection Reports
Workflow Diagram

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🚀 Quick Start Guide

To run this project locally:

1. Install Dependencies:

   pip install -r requirements.txt

2. Run the Application:

   python main.py

3. Access the Dashboard: Open http://localhost:5000 in your browser.
   • Default Credentials: admin / password

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👨‍🎓 Project Information

This project was completed as part of the requirements for a Bachelor's degree in Cybersecurity.

• Academic Institution: University of Science and Technology.
• e-mail: [alaqlqlan47@gmail.com ]
• Developed by: [khaled abdulsattar]

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⚖️ License

This project is licensed under the MIT License. See the [MIT License.md] file for details.