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