## 5 Discussion
### Future Research Directions  
Based on our findings, we identify several promising directions for future research:  
* Enhanced Anomaly Detection: Developing more sophisticated anomaly detection models that can identify subtle attacks while maintaining low false positive rates. This could include exploring deep learning architectures specifically designed for time series sensor data and incorporating contextual information from multiple sensors [45].
* Sensor Fusion for Security: Investigating how data from complementary sensors (RGB, DVS, LiDAR, and radar) can be fused not only for improved perception but also as a security mechanism [61]. Inconsistencies between different sensor modalities could serve as indicators of potential attacks [47].
* Graceful Degradation: Designing autonomous systems that can gracefully degrade performance when attacks are detected rather than failing catastrophically. This could involve developing fallback driving policies that rely on uncompromised sensors or implementing safe stop procedures [7].
* Human-in-the-Loop Security: Exploring how human operators could be effectively integrated into the security framework, particularly for remote monitoring and intervention when anomalies are detected [60]. This raises important questions about interface design, situation awareness, and response time.
* DVS Integration: Further investigating the potential of DVS technology for enhancing autonomous vehicle security, including developing specialized perception algorithms that leverage the unique properties of event-based vision [28] and exploring hybrid RGB-DVS architectures.
* Standardized Security Evaluation: Developing standardized benchmarks and evaluation methodologies for assessing the security of autonomous vehicle sensor systems, enabling more systematic comparison of different defense approaches [62].  
Addressing these research directions will require interdisciplinary collaboration between experts in computer vision, cybersecurity, autonomous systems, and human factors [51]. By advancing our understanding of sensor vulnerabilities and developing more robust defense mechanisms, we can enhance the security and trustworthiness of autonomous vehicles as they transition from research laboratories to public roads.  
**Author Contributions:** Conceptualization, M.S.; methodology, M.S., K.S., and M.K.S.O.; software, M.S., K.S., and M.K.S.O.; validation, M.S., K.S., and M.K.S.O.; formal analysis, M.S., K.S., and M.K.S.O.; investigation, M.S. and K.S.; resources, M.S.; data curation, K.S. and M.K.S.O.; writing--original draft preparation, M.S., K.S., and M.K.S.O.; writing--review and editing, M.S. and M.W.; visualization, M.S. and K.S.; supervision, M.S. and M.W.; project administration, M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The code for experiments is accessed on 29 June 2025. The data presented in this study are openly available in Robust-Sensor-Cybersecurity-for-Autonomous-Vehicles repository at [[https://github.com/[PERSON]/Robust-Sensor-Cybersecurity-for-Autonomous-Vehicles](https://github.com/[PERSON]/Robust-Sensor-Cybersecurity-for-Autonomous-Vehicles)]([https://github.com/[PERSON]/Robust-Sensor-Cybersecurity-for-Autonomous-Vehicles](https://github.com/[PERSON]/Robust-Sensor-Cybersecurity-for-Autonomous-Vehicles)).  
**Acknowledgments:** The authors would like to thank all individuals and institutions who supported this work indirectly.  
**Conflicts of Interest:** The authors declare no conflicts of interest.