## 1 Introduction
In this study, a hybrid artificial intelligence model, called 1 DCNN-DT, is proposed to detect DDoS attacks targeting the SDN controller in the SD-VANET architecture. To effectively extract features from low-volume DDoS traffic data, a Convolutional Neural Network (CNN) architecture is employed in the hybrid model. The proposed security system, named SD-VANET_Guard, is designed to operate as a module within the SDN network controller. SD-VANET_Guard, working within the SDN network controller, provides an attack detection methodology that includes collecting network traffic data, processing these data, and classifying network traffic. The contributions of this study to the literature are as follows:  
_Contribution to Scalability and Flexibility in Intelligent Transportation Systems:_ This study highlights the challenges faced by the traditional VANET architecture in terms of scalability, flexibility, and management and proposes a new architecture called SD-VANET that utilizes the Software Defined Networking (SDN) approach as a solution to these issues. Thisarchitecture can potentially adapt to the future needs of intelligent transportation systems. In addition, SDN technology optimizes the charging requirements of electric vehicles, reducing latency and providing a faster and more efficient charging process. _Innovative Solution Proposal for Security Issues in SD-VANET Architecture:_ This study addresses potential security vulnerabilities in the SD-VANET architecture, which combines Software Defined Networking (SDN) and Vehicular Ad Hoc Networks (VANET). It proposes a hybrid artificial intelligence model (1 DCNN-DT) specifically designed to effectively detect DDoS attacks targeting the SD-VANET controller. _Real-Time DDoS Detection and Traffic Monitoring:_ The research develops the SD-VANET_Guard security system, which can be seamlessly integrated into SD-VANET systems to accurately detect DDoS attacks and monitor network traffic in real-time. This security system enhances the reliability of SD-VANET by achieving high detection rates even under low-volume attack traffic. _Hybrid Attack Detection Approach Utilizing Artificial Intelligence:_ The proposed hybrid AI model combines a 1-Dimensional Convolutional Neural Network (1 DCNN) and Decision Tree algorithms to effectively extract attack data from network traffic. This hybrid approach provides higher accuracy and faster detection compared to traditional methods. _Evidence of Effectiveness Through Experimental Results:_ The study tests the performance of the proposed model using real experimental data and demonstrates an impressive detection accuracy of approximately 90% for DDoS attacks. These results indicate that the proposed system is a promising solution for detecting attacks on the SD-VANET architecture. _Contribution to Security in Intelligent Transportation Systems:_ This research emphasizes the critical importance of security measures in the future of intelligent transportation systems by presenting a new defense mechanism that enhances the resilience of the SD-VANET architecture against cyber threats. This contribution is particularly relevant for applications involving autonomous vehicles and smart cities. While SD-VANET is indeed an established technology and various models, including CNN and Decision Tree models, have been used in the detection of DDoS attacks, this study introduces a hybrid approach tailored specifically for real-time DDoS detection within SD-VANETs. Our model, 1D-CNN combined with Decision Tree (1 DCNN-DT), is designed to address the unique network topology and dynamic communication environment inherent in SD-VANETs. Additionally, the SD-VANET_Guard system integrates this hybrid AI approach with real-time traffic monitoring, enhancing both detection speed and accuracy compared to traditional methods. The proposed model not only identifies DDoS attacks with high precision but also demonstrates resilience against the high variability in traffic flow within SD-VANET, an area that has received limited attention in existing literature.