🎓 Graduation Project — B.Sc. in Information Technology Faculty of Computer and Information, Kafrelsheikh University — 2024/2025
A real-time cybersecurity system designed to detect and mitigate DDoS attacks in smart home and IoT environments using Software-Defined Networking (SDN), Deep Learning, and Blockchain.
The system combines centralized network control with intelligent traffic analysis to automatically identify malicious behavior and respond instantly.
Smart home and IoT environments are highly vulnerable to DDoS attacks due to:
- Limited device resources
- Lack of centralized security control
- Increasing exposure to internet-based threats
This project introduces a multi-layered defense system that:
- Monitors network traffic in real-time using SDN
- Detects anomalies using Deep Learning (RNN / LSTM)
- Automatically mitigates attacks via flow control and rate limiting
- Sends real-time alerts to users and administrators
- Logs security events securely using Blockchain
┌─────────────────────────────────────────┐
│ Mobile Application (Flutter) │
│ Real-time Alerts & User Interface │
├─────────────────────────────────────────┤
│ SDN Controller (Ryu) + Detection Engine │
│ - Deep Learning (RNN / LSTM) │
│ - Threat Intelligence │
│ - Blockchain Integration │
├─────────────────────────────────────────┤
│ Infrastructure Layer (Mininet / IoT) │
│ Smart Devices & Network Simulation │
└─────────────────────────────────────────┘
Traffic → SDN Controller → Feature Extraction → ML Model → Detection → Mitigation → Alerts → Blockchain Logging
- 🔍 Real-time DDoS & SYN Flood detection
- ⚙️ SDN-based centralized traffic control (Ryu + Mininet)
- 🧠 Deep Learning models (RNN / LSTM) for anomaly detection
- 🛡️ Automated mitigation (Rate Limiting & Flow Rules)
- 📱 Mobile App (Flutter + Firebase) for live alerts
- 💬 Discord alerts for security administrators
- 🔗 Blockchain for tamper-proof logging and authentication
- 🔒 Secure communication via MQTT with encryption
- SDN (Software-Defined Networking)
- Ryu Controller
- Mininet
- MQTT (Encrypted Communication)
- Deep Learning (RNN / LSTM)
- Feature Engineering & Traffic Analysis
- Threat Intelligence Integration
- Smart Contracts
- Event Logging (Tamper-proof)
- Web3 Integration
- Flutter Mobile App
- Firebase (Firestore + Authentication)
- Discord Webhooks
├── sdn/
├── ml_models/
├── blockchain/
├── mobile_app/
├── mqtt/
└── docs/
👉 Watch the system in action:
- Attack simulation
- Real-time detection
- Automated mitigation
- Mobile + Discord alerts
[https://drive.google.com/file/d/1VBc170znCiy0tgcX9ES3Z5MM5MeFmO2E/view?usp=drive_link]
# Clone repository
git clone https://github.com/Bassma-Mosa/Graduation-Project.git
cd Graduation-Project
# Install dependencies
pip install -r requirements.txt
# Run SDN controller
ryu-manager sdn/ryu_controller/ddos_mitigation.py
# Start Mininet topology
sudo python3 sdn/mininet_topology/smart_home_topo.py- High detection accuracy using deep learning models
- Real-time mitigation using SDN flow control
- Effective defense against SYN Flood and DDoS attacks
- Secure and immutable logging using Blockchain
This project demonstrates practical experience in:
- Cybersecurity (DDoS Detection & Mitigation)
- SDN Network Control
- AI in Security Systems
- Real-time System Design
- Secure System Architecture