## 2 Related Work  
Machine learning techniques have been widely used in the literature to address classification problems, such as Distributed Denial of Service (DDoS) attacks. Prominentclassification algorithms that have been widely used in numerous studies include Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Decision Tree (DT). These methods have shown promising results in various contexts. In addition, some studies have adopted deep learning-based approaches and implemented feature selection and dimensionality reduction strategies to accurately classify attacks. Despite the advances in machine learning and deep learning techniques, there is a notable lack of studies focused on real-time detection of DDoS attacks specifically targeting Software Defined Vehicular Ad Hoc Networks (SD-VANETs). Most existing work focuses on classifying and detecting attacks using pre-existing datasets, limiting their applicability to dynamic and real-time environments. A review of the literature reveals that while traditional machine learning methods have been effectively applied to DDoS detection, there is a significant gap in the exploration of real-time detection frameworks in the context of the SD-VANET architecture. Recent studies have primarily utilized static datasets, which may not adequately represent the dynamic nature of network traffic and the evolving tactics of attackers. This highlights the need for further research that utilizes real-time data collection and advanced machine learning techniques to improve the resilience of SD-VANETs against DDoS threats. [PERSON] et al. pointed out that existing DDoS prevention mechanisms are not effective enough. In their study, they investigated the impact of _misbehavior_ and _newflow_ attacks on SDN architecture by periodically collecting traffic data from forwarding devices in the data plane and applying machine learning classification algorithms. The goal was to develop a system capable of responding quickly to sudden traffic changes in the SDN architecture. The designed system consists of three modules: a machine learning module, feature selection, and the model training, testing, and prediction phases. The study focused on Packet_In messages between the controller and forwarding devices during an attack, and classification algorithms such as SVM, J48, and Naive Bayes were used [10]. [PERSON] et al. developed a software-based platform for vehicular networks. In this platform, attack detection is performed based on OFPT_PACKET_IN messages and flow table entries between the controller and the data plane. Using the SVM classifier, flow table entries were trained, and these entries were combined in the data acquisition module for real-time attack detection. The features of the flow entries were extracted and sent to the SVM training module to determine whether the entries were benign or malicious. In addition, a triggering module was created to reduce the number of OFPT_PACKET_IN messages, and the controller response time was shortened when a certain threshold was exceeded [11]. [PERSON] et al. developed a deep learning model for flow-based anomaly detection. The Deep Neural Network (DNN) model was applied to attack detection, and the results showed that the deep learning approach has a strong potential for flow-based anomaly detection in SDNs [12]. [PERSON] et al. emphasized that cyberattacks are a major threat to evolving computer networks. In particular, they noted that the integration of SDN architecture with artificial intelligence methods provides significant advantages in detecting and preventing DDoS attacks. In their study, they proposed a CNN-based system to detect and verify attacks using a flow-based dataset [13]. [PERSON] et al. compared traditional machine learning algorithms and deep learning models for detecting attacks targeting the software-defined network controller. First, the Random Forest (RF) algorithm was applied to the NSL-KDD dataset, and feature selection was performed to improve accuracy.