## 1 Introduction
While this attack was not specifically targeted at IoV systems, it underlines the vulnerability of interconnected networks to high-volume DDoS attacks. This could potentially disrupt the Internet of Vehicles when vehicle-to-infrastructure (V2I) or vehicle-to-vehicle (V2V) networks become jammed, hence compromising safety-critical functions and communications within smart transportation systems [7,8]. The impacts of a successful DDoS attack on the IoV are the following: There are monetary risks involved because of the service disruptions and the necessity of response and recovery services [9]. The reputation of manufacturers and service providers could be impacted, mainly due to crises in consumer confidence in smart vehicle systems. Furthermore, interruption of service can have quick and perhaps lethal consequences; for instance, it can slow down emergency response vehicles or trigger traffic mishaps arising from failed communication gadgets [10]. The effective upgrade in mobility and automotives can be brought by the integration of AI, automation, and IoV technologies; however, enhanced security measures are required to address threats such as DDoS attacks [11]. As connectivity and data exchange are becoming critical in smart vehicles, the potential risks and attacks must be detected and mitigated to ensure future transport systems' security and functionality. DDoS attacks are a major threat to IoV, which is the interconnection of vehicles, devices, and infrastructure for reducing the rate of accidents, increasing efficiency, and improving the transport experience [12]. While the cascaded anomaly detection systems allow for the reduction of data transmission to the central systems by only forwarding essential data, they would still internally require a certain degree of central processing, which might be hard for scalability and privacy. On the other hand, our approach with FL fully decentralizes the detection process because model training in IoV devices happens right on the devices. It also further minimizes data transmission requirements, enforces scalability, and preserves the privacy of data, which are crucial in the distributed and data-sensitive environment present in the Internet of Vehicles [13]. Considering the intricacy and the size of the IoV networks, the traditional detection methods often fail to mitigate the impact of DDoS threats. Accordingly, this paper proposes a new way of doing things by combining Gini index-based feature selection with Federated Learning (FL), which is scalable and protects privacy when detecting DDoS attacks. Moreover, while the number of connected vehicles is on the rise, DDoS attacks are equally on the rise, as well as the consequences of such attacks. This is because centralized systems become overloaded with massive amounts of data and cannot grow and change to catch up with the nature of IoV networks, which are distributed and complex. This calls for new methods that would enable the detection methods to operate faster and protect data against any suspicious endorsements [14]. Such issues have to be addressed so that the adoption of smart vehicle technologies will be welcomed by the public and that some operational safe transport systems will be relied upon in the future [15]. To address these challenges, this research offers a new technique that uses A Gini index as a special approach to feature selection for enabling the application of Federated Learning FL to enhance the DDoS attack detection mechanism in IoV systems [16]. Feature selection with the Gini index is employed on the features of a given dataset to lessen the dimensionality of a particular feature space when machine learning systems are designed. When it comes to FL, it is possible to have a model running on multiple devices, which obtains and learns new data while preserving privacy and is able to scale [17; 18].