Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses
arXiv:2502.04201 · doi:10.1016/j.cose.2025.104352
Abstract
The advancements in autonomous driving technology, coupled with the growing interest from automotive manufacturers and tech companies, suggest a rising adoption of Connected Autonomous Vehicles (CAVs) in the near future. Despite some evidence of higher accident rates in AVs, these incidents tend to result in less severe injuries compared to traditional vehicles due to cooperative safety measures. However, the increased complexity of CAV systems exposes them to significant security vulnerabilities, potentially compromising their performance and communication integrity. This paper contributes by presenting a detailed analysis of existing security frameworks and protocols, focusing on intra- and inter-vehicle communications. We systematically evaluate the effectiveness of these frameworks in addressing known vulnerabilities and propose a set of best practices for enhancing CAV communication security. The paper also provides a comprehensive taxonomy of attack vectors in CAV ecosystems and suggests future research directions for designing more robust security mechanisms. Our key contributions include the development of a new classification system for CAV security threats, the proposal of practical security protocols, and the introduction of use cases that demonstrate how these protocols can be integrated into real-world CAV applications. These insights are crucial for advancing secure CAV adoption and ensuring the safe integration of autonomous vehicles into intelligent transportation systems.
References in corpus (19)
- Survey of Important Issues in UAV Communication Networks
- Deep Reinforcement Learning framework for Autonomous Driving
- BlockChain: A distributed solution to automotive security and privacy
- Poisoning Attacks against Support Vector Machines
- Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks
- Secure Vehicular Communication Systems: Design and Architecture
- Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving
- Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems
- Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
- Vehicular Communications: Survey and Challenges of Channel and Propagation Models
- NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles
- Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks
- DARTS: Deceiving Autonomous Cars with Toxic Signs
- Blockchain-enabled Authentication Handover with Efficient Privacy Protection in SDN-based 5G Networks
- DPatch: An Adversarial Patch Attack on Object Detectors
- Blockchain Based Intelligent Vehicle Data sharing Framework
- Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles
- A Federated Learning Approach for Multi-stage Threat Analysis in Advanced Persistent Threat Campaigns
- Systemization of Knowledge (SoK)- Cross Impact of Transfer Learning in Cybersecurity: Offensive, Defensive and Threat Intelligence Perspectives