## 3 Ad Hoc Networks  
Ad Hoc Networks are networks in which devices communicate directly without the need for a centralized access point. Originally developed for military operations, these networks provide a communication solution that does not require infrastructure between mobile units. As these networks evolved, Mobile Ad Hoc Networks (MANETs) emerged to provide a more dynamic and scalable structure. While MANETs offer advantages in scenarios such as emergencies and military operations, they face challenges related to security, performance, and management [19]. The limitations of MANETs, along with advances in wireless networking technologies, led to the concept of Vehicular Ad-Hoc Networks (VANETs). VANET enables wireless communication between vehicles and is used in traffic management, safety, and autonomous vehicle systems. VANET consists of components such as Road Side Units (RSUs), On Board Units (OBUs), and Application Units (AUs) (Figure 1). RSUs are fixed roadside nodes that communicate with vehicles, while OBUs collect and process data from in-vehicle sensors. AUs provide an interface to transmit this data to users [4]. The innovations offered by VANET play a critical role in areas such as smart cities and autonomous vehicles. In VANETs, communication faces several unique challenges due to the inherent characteristics of vehicular environments. High mobility, caused by the continuous movement of vehicles, results in frequent topology changes that can disrupt communication paths and make maintaining consistent network connections more difficult [20]. Additionally, varying network densities, ranging from sparse connectivity in rural areas to highly dense networks in urban regions, further complicate network management [21]. These dynamic conditions impact communication efficiency and introduce significant challenges for real-time data processing and cybersecurity within VANETs. This study builds upon the SD-VANET framework to address these specific challenges, an innovative approach that integrates SDN principles with VANETs [22]. By centralizing network control, SD-VANET enhances overall scalability, security, and flexibility, allowing for more adaptive and responsive network management [23]. SDN's centralized control framework is particularly beneficial for VANET environments, as it allows for streamlined decision-making processes and quicker adjustments to rapid topology changes. This centralized approach also facilitates better handling of security measures, which are critical in VANETs due to the heightened risk of cyber threats, such as DDoS attacks, that can exploit the network's dynamic nature [24]. Our approach utilizes a hybrid model that combines a one-dimensional 1D-CNN with a Decision Tree to effectively detect and mitigate DDoS attacks within the SD-VANET environment. The 1D-CNN, integrated within the SD-VANET architecture, serves as a powerful tool for feature extraction, enabling it to capture critical patterns from traffic flow data even in conditions of high mobility and network density. This deep learning component is capable of identifying complex relationships within the data, helping to distinguish between normal traffic and potentially malicious flows. Meanwhile, the Decision Tree model complements this by providing rapid classification capabilities, which are essential for real-time anomaly detection. The Decision Tree's straightforward and computationally efficient structure allows the system to process data quickly, ensuring that the detection mechanism remains effective in dynamic and time-sensitive VANET environments. By incorporating SDN into an ad hoc vehicular network, our proposed method achieves centralized, efficient traffic management along with robust cybersecurity measures. This combination addresses both the scalability and security needs of modern vehicular networks.