… that exploits Cooperative Awareness Messages (CAMs), which cars use to share their location and speed. We propose an approach for a distributed Intrusion Detection System (IDS) designed to mitigate DDoS attacks in a connected car environment. Initially, we assessed the impact of CAM-DDoS attacks on connected cars through various simulation scenarios. These scenarios revealed a decrease in the CAM broadcast rate from legitimate cars and an accumulation of invalid data. Subsequently, we proposed a distributed architecture for an IDS and assessed the effectiveness of three supervised ensemble learning algorithms against these attacks. Given the challenge of generalizability in machine learning models, we also evaluated the robustness of our DDoS detection approach against other types of DDoS attacks, such as JamDDoS. The results show that all DDoS messages were processed uniformly, which explains the models’ robust performance against studied DDoS attacks. Additionally, we demonstrated how a DDoS attack can be transformed into a Sybil DDoS attack that avoids DDoS detectors in a safety-critical connected car environment. To address this, we developed and evaluated an optimized data-handling approach that serves as the foundation for our proposed ensemble hard voting detection model. The results obtained demonstrate an effective attack detection with reduced false alarms against Sybil DDoS attacks.