## 2 Related Work  
The cybersecurity of autonomous vehicles (AVs) is a rapidly evolving field, with extensive research focused on identifying vulnerabilities and developing robust defense mechanisms. This section situates our work within the existing literature, focusing on three key areas: security for multi-sensor fusion systems, recent advances in anomaly detection [46], and the nascent field of dynamic vision sensor (DVS) security. By comparing our approach to recent studies, we highlight the novelty and specific contributions of our research. Recent studies have increasingly focused on the vulnerabilities of multi-sensor fusion algorithms, which are critical for robust perception in AVs. For instance, recent work by [PERSON] et al. [47] investigates malicious attacks that specifically target the fusion mechanism, creating inconsistencies between LiDAR and camera data to deceive the perception system. Similarly, other research has explored how to generate \"invisible\" adversarial attacks that can simultaneously fool both camera and LiDAR systems [48]. While these studies provide crucial insights into the security of integrated systems, they primarily focus on attacking the central fusion logic. In contrast, our work provides a more granular analysis by evaluating the resilience of individual sensors before their data is fed into a fusion model. We systematically implement seven distinct attack vectors on a sensor-by-sensor basis, establishing a foundational understanding of how specific sensor modalities degrade. This approach is complementary and essential, as a resilient fusion system still depends on the integrity of its input data streams. In parallel with security-focused research, recent work has explored the use of end-to-end reinforcement learning for developing AV control policies in simulation environments [49]. [PERSON] and [PERSON] [50] demonstrated that escape maneuvers in urban driving scenarios can be learned using neural policy architectures trained entirely in CARLA. While their work focused on control under dynamic conditions, our study shifts attention to the sensor-level cybersecurity of perception inputs that feed into such learning-based agents. The development of holistic security architectures, often leveraging artificial intelligence (AI) and blockchain, represents another significant research trend. [PERSON] et al. [51]propose a comprehensive framework that uses AI for threat detection and blockchain for secure data exchange. Surveys on AI-based intrusion detection systems further highlight the popularity of machine learning for identifying anomalous vehicle behavior [52]. Our research builds upon this trend by implementing and evaluating a specific, state-of-the-art unsupervised anomaly detection model, EfficientAD, from the anomalib library [44]. A key differentiator of our study is that we train and test this model on both conventional RGB data and, uniquely, on DVS event streams. By demonstrating over 95% detection accuracy, we not only validate the model's effectiveness but also provide a direct, quantitative comparison of its performance on these two distinct sensor types, which, to our knowledge, has not been systematically explored in this context. Research into emerging sensors like DVS has gained significant traction, primarily for their performance advantages in challenging conditions. The work by [PERSON] and [PERSON] [32] highlights the benefits of event cameras for low-latency automotive vision, while other studies focus on their high dynamic range and reduced motion blur [28]. [PERSON] et al. [53] further demonstrated the effectiveness of DVS in real-time pedestrian detection under adverse weather, reinforcing its practical value in safety-critical AV scenarios. However, the vast majority of DVS research is centered on perception algorithms and performance benchmarks rather than cybersecurity resilience. Our paper addresses this critical gap.