Project Overview Designed and implemented an intelligent DDoS detection and mitigation framework for Software-Defined Networking (SDN) based Vehicular Ad-hoc Networks (VANETs) to ensure secure, reliable, and low-latency vehicle-to-infrastructure (V2I) communication. The system leverages SDN’s centralized control to dynamically monitor network traffic and mitigate DDoS attacks in real time, ensuring uninterrupted safety-critical vehicular services. Key Innovations: SDN-Based Centralized Security Control OpenFlow-enabled SDN controller Global view of VANET traffic behavior Intelligent DDoS Detection Flow-level traffic analysis Anomaly detection using ML / Deep Learning (LSTM Autoencoder / Hybrid model) Real-Time Mitigation Strategy Malicious vehicle flow isolation Dynamic rule installation at RSUs & switches Rate limiting and blacklisting of attack sources VANET-Specific Security Awareness Handles high mobility and dynamic topology Maintains low latency for safety messages Technologies Used SDN Controller: Ryu / ONOS VANET Simulator: SUMO + Mininet-WiFi ML/DL Model: LSTM Autoencoder / SVM / Random Forest/ use other technique than LSTM can use multimodel for transfer learning Dataset: SDN Specifc dataset Protocols: OpenFlow, TCP/UDP Programming: Python Performance Metrics Detection Accuracy: High (>95%) False Positive Rate: Low Mitigation Delay: Minimal Network Throughput & PDR: Improved post-mitigation Impact & Use Cases ✔ Secure smart transportation systems ✔ Prevention of service disruption in V2X communication ✔ Applicable to Smart Cities, ITS, Autonomous Vehicles