## 7 Conclusions  
This study presents a novel security solution for SD-VANET (Software Defined Vehicular Ad Hoc Networks) to counter DDoS (Distributed Denial of Service) attacks in intelligent transportation systems. By combining the flexibility of Software Defined Networks (SDN) with the dynamic communication capabilities of VANET, SD-VANET provides an innovative framework that meets the growing demands of smart transportation infrastructures. However, as emphasized in this study, the centralized SDN controller in SD-VANET introduces significant security vulnerabilities, particularly against DDoS attacks. To mitigate this risk, the proposed SD-VANET_Guard system utilizes a hybrid artificial intelligence model that integrates 1D-CNN with Decision Trees (DT) for DDoS attack detection. Experimentalresults indicate that the system can identify approximately 90% of malicious DDoS traffic, demonstrating the effectiveness of the SD-VANET_Guard system in enhancing resilience against such attacks. To further contextualize the performance of the SD-VANET_Guard system, a comparison with relevant studies in the field is provided in Table 4. This table highlights key metrics, methodologies, and results from similar approaches, allowing readers to understand the relative strengths and unique contributions of this study's hybrid 1D-CNN and Decision Tree model. By positioning our findings alongside existing research, we emphasize how SD-VANET_Guard addresses the real-time detection needs of SD-VANET environments while mitigating vulnerabilities specific to DDoS attacks in intelligent transportation systems. While several studies have explored the detection of DDoS attacks within SD-VANET and similar network environments, this manuscript presents a unique approach by focusing on a hybrid 1D-CNN and Decision Tree (DT) model specifically optimized for the real-time detection of DDoS attacks in the dynamic SD-VANET environment. Unlike other studies that primarily focus on static datasets or general IoT networks, our model addresses the high variability of traffic flows within SD-VANET and provides a low-latency solution tailored for intelligent transportation systems. Additionally, the integration of the SD-VANET_Guard system directly into the SDN controller allows for efficient processing and rapid response to DDoS attacks, which distinguishes this study from prior work. This work, therefore, fills a critical gap in SD-VANET security by enhancing detection accuracy and response time under realistic network conditions. In conclusion, this study addresses the scalability and management challenges inherent in traditional VANET architectures through the use of SDN while simultaneously providing a robust security framework to safeguard future intelligent transportation systems from cyber threats. The integration of AI-based attack detection in SD-VANET networks represents a promising approach for both academic research and practical applications in intelligent transportation and network security. Conceptualization, O.P., S.O. and M.T. ; methodology, MT., O.P. and F.Y. ; software, A.A. and S.O. ; validation, H.P., M.T. and S.O. ; formal analysis, A.A., O.P., F.Y. and M.T. ; investigation, H.P., M.T., F.Y., O.P. and S.O. ; resources, O.P. and F.Y. ; data curation, O.P. and M.T. ; writing original draft preparation, M.T., S.O. and A.A.; writing review and editing, H.P., F.Y. and S.O. ; visualization, S.O. and M.T. ; supervision, O.P. All authors have read and agreed to the published version of the manuscript. This paper is supported by the European Union's Horizon Europe Research and Innovation Programme under grant agreement No. 101095863, project FLEXSHIP. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.