A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles
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Abstract (arXiv)
Autonomous Vehicle has been transforming intelligent transportation systems. As telecommunication technology improves, autonomous vehicles are getting connected to each other and to infrastructures, forming Connected and Autonomous Vehicles (CAVs). CAVs will help humans achieve safe, efficient, and autonomous transportation systems. However, CAVs will face significant security challenges because many of their components are vulnerable to attacks, and a successful attack on a CAV may have significant impacts on other CAVs and infrastructures due to their communications. In this paper, we conduct a survey on 184 papers from 2000 to 2020 to understand state-of-the-art CAV attacks and defense techniques. This survey first presents a comprehensive overview of security attacks and their corresponding countermeasures on CAVs. We then discuss the details of attack models based on the targeted CAV components of attacks, access requirements, and attack motives. Finally, we identify some current research challenges and trends from the perspectives of both academic research and industrial development. Based on our studies of academic literature and industrial publications, we have not found any strong connection between academic research and industry's implementation on CAV-related security issues. While efforts from CAV manufacturers to secure CAVs have been reported, there is no evidence to show that CAVs on the market have the ability to defend against some novel attack models that the research community has recently found. This survey may give researchers and engineers a better understanding of the current status and trend of CAV security for CAV future improvement.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Jul 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 0; 2025: 5; 2026: 1.
10 papers.
- 2021Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defensescited by 30
- 2023On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasurescited by 11
- 2023Trust-Aware Resilient Control and Coordination of Connected and Automated Vehiclescited by 2
- 2025Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defensescited by 1
- 2025Advancing Autonomous Driving System Testing: Demands, Challenges, and Future Directionscited by 1
- 2022Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehiclescited by 0
- 2025SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networkscited by 0
- 2025BiTAA: A Bi-Task Adversarial Attack for Object Detection and Depth Estimation via 3D Gaussian Splattingcited by 0
- 2025Studying Various Activation Functions and Non-IID Data for Machine Learning Model Robustnesscited by 0
- 2026Robust Vision Systems for Connected and Autonomous Vehicles: Security Challenges and Attack Vectorscited by 0
- 2026Robust Vision Systems for Connected and Autonomous Vehicles: Security Challenges and Attack Vectors10 Feb 2026
- 2025Advancing Autonomous Driving System Testing: Demands, Challenges, and Future Directions9 Dec 2025
- 2025Studying Various Activation Functions and Non-IID Data for Machine Learning Model Robustness3 Dec 2025
- 2025BiTAA: A Bi-Task Adversarial Attack for Object Detection and Depth Estimation via 3D Gaussian Splatting24 Sep 2025
- 2025SDN-Based False Data Detection With Its Mitigation and Machine Learning Robustness for In-Vehicle Networks6 Jun 2025
- 2025Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses6 Feb 2025
- 2023On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures22 Sep 2023
- 2023Trust-Aware Resilient Control and Coordination of Connected and Automated Vehicles26 May 2023
- 2022Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles15 Dec 2022
- 2021Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses5 Apr 2021