Applying Machine Learning to Detect DDoS Attacks in SD-VANET Networks: Applying Machine Learning to Detect DDoS Attacks in SD-VANET Networks - حسن, بسام (Latakia University Journal -Engineering Sciences Series, 2025)
Vehicular Ad Hoc Network (VANET) is a modern technology that integrates next-generation wireless network capabilities into vehicles. Integrating Software-Defined Networking (SDN) with VANETs is a significant step towards developing intelligent vehicular networks. This new architecture offers innovative solutions to VANET challenges in various fields, especially in security.Using machine learning to detect Distributed Denial of Service (DDoS) attacks in Software-Defined VANETs (SD-VANET) is a modern and effective technique. This approach relies on analyzing network traffic patterns and detecting abnormal behaviors that indicate DDoS attacks. Machine learning algorithms can quickly and accurately identify these patterns, helping to take preventive measures to protect the network. In this research, an SDN-VANET environment was created using the Mininet-WiFi emulator, with the Ryu controller providing software components and APIs that facilitate developers in creating new applications for network management and control. Statistical information extracted from flow tables was used to detect DDoS attacks targeting controllers in the SDN-VANET environment. The dataset used to train the machine learning model was obtained from the experimentally designed SD-VANET architecture, and the best-performing parameters for the SVM classifier were used during the training phase, resulting in high accuracy.