SDN-VANET DDoS Detection Module
Budget: ₹1,500 – ₹12,500 INR
I’m looking for a freelancer with expertise in Machine Learning, Deep Learning, and Software Defined Networking (SDN) to develop a complete project titled:
“Intelligent Detection Mechanism for DDoS Attack Prevention in SDN-VANET.”
The goal is to design and implement an intelligent, ML/DL-based detection and mitigation system that can detect Distributed Denial of Service (DDoS) attacks in SDN-enabled Vehicular Ad-hoc Networks (VANETs).
1. Detection Module:
Use LSTM Autoencoder (unsupervised) or any efficient deep learning algorithm for DDoS anomaly detection.
Optionally integrate Federated Learning (FedAvg) across RSUs/OBUs to simulate distributed learning without sharing raw data.
Use datasets like CIC-DDoS2019, CIC-IDS2017, or CAIDA DDoS dataset for training and evaluation.
Implement feature extraction (e.g., via CICFlowMeter or pcap analysis).
2. Mitigation Module:
Build an SDN controller app (Ryu or ONOS) that enforces mitigation (drop rules, rate limiting, rerouting) dynamically.
Integrate Reinforcement Learning (RL) (DQN/PPO) at controller level to automate mitigation decision-making.
3. Simulation / Environment Setup:
Integrate with Mininet and/or Veins (OMNeT++ + SUMO) for VANET simulation.
Generate vehicular traffic, simulate attack scenarios (UDP flood, TCP-SYN flood), and evaluate system performance.
Expected Deliverables:
Complete source code (Python preferred).
Trained model files and instructions to re-train.
Simulation setup (Mininet + Ryu or OMNeT++ + Veins).
Report/Documentation explaining algorithms, architecture, results, and metrics.
Dataset preprocessing scripts.
Evaluation metrics:
Accuracy, Precision, Recall, F1-score, AUC
Detection latency, CPU/memory overhead, packet loss, mitigation efficiency.
Technical Stack (Preferred):
Python (PyTorch / TensorFlow)
Ryu SDN Controller
Mininet / Veins / SUMO
CICFlowMeter for feature extraction
Linux-based environment
Duration: 2-3 weeks
Deliverables: Weekly progress updates
Final Output: Working code, report, and demo video
“Intelligent Detection Mechanism for DDoS Attack Prevention in SDN-VANET.”
The goal is to design and implement an intelligent, ML/DL-based detection and mitigation system that can detect Distributed Denial of Service (DDoS) attacks in SDN-enabled Vehicular Ad-hoc Networks (VANETs).
1. Detection Module:
Use LSTM Autoencoder (unsupervised) or any efficient deep learning algorithm for DDoS anomaly detection.
Optionally integrate Federated Learning (FedAvg) across RSUs/OBUs to simulate distributed learning without sharing raw data.
Use datasets like CIC-DDoS2019, CIC-IDS2017, or CAIDA DDoS dataset for training and evaluation.
Implement feature extraction (e.g., via CICFlowMeter or pcap analysis).
2. Mitigation Module:
Build an SDN controller app (Ryu or ONOS) that enforces mitigation (drop rules, rate limiting, rerouting) dynamically.
Integrate Reinforcement Learning (RL) (DQN/PPO) at controller level to automate mitigation decision-making.
3. Simulation / Environment Setup:
Integrate with Mininet and/or Veins (OMNeT++ + SUMO) for VANET simulation.
Generate vehicular traffic, simulate attack scenarios (UDP flood, TCP-SYN flood), and evaluate system performance.
Expected Deliverables:
Complete source code (Python preferred).
Trained model files and instructions to re-train.
Simulation setup (Mininet + Ryu or OMNeT++ + Veins).
Report/Documentation explaining algorithms, architecture, results, and metrics.
Dataset preprocessing scripts.
Evaluation metrics:
Accuracy, Precision, Recall, F1-score, AUC
Detection latency, CPU/memory overhead, packet loss, mitigation efficiency.
Technical Stack (Preferred):
Python (PyTorch / TensorFlow)
Ryu SDN Controller
Mininet / Veins / SUMO
CICFlowMeter for feature extraction
Linux-based environment
Duration: 2-3 weeks
Deliverables: Weekly progress updates
Final Output: Working code, report, and demo video