# Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses

arXiv: https://arxiv.org/abs/2502.04201 · HTML: https://mlsanity.com/paper/2502.04201/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 201 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 26 of 26)

- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Deep Reinforcement Learning framework for Autonomous Driving](https://mlsanity.com/paper/1704.02532/) (2017)
- [Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving](https://mlsanity.com/paper/1907.06826/) (2019)
- [Adversarial examples for generative models](https://mlsanity.com/paper/1702.06832/) (2017)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning](https://mlsanity.com/paper/1709.05750/) (2017)
- [Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward](https://mlsanity.com/paper/1905.12762/) (2019)
- [Survey of Important Issues in UAV Communication Networks](https://mlsanity.com/paper/1603.08462/) (2016)
- [Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks](https://mlsanity.com/paper/1807.05720/) (2018)
- [Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems](https://mlsanity.com/paper/1805.00983/) (2018)
- [Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-based IIoT Networks](https://mlsanity.com/paper/2204.09829/) (2022)
- [A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2007.08041/) (2020)
- [A Systematic Survey of Control Techniques and Applications in Connected and Automated Vehicles](https://mlsanity.com/paper/2303.05665/) (2023)
- [Survey on Misbehavior Detection in Cooperative Intelligent Transportation Systems](https://mlsanity.com/paper/1610.06810/) (2016)
- [Blockchain-inspired Event Recording System for Autonomous Vehicles](https://mlsanity.com/paper/1809.04732/) (2018)
- [Autonomous Vehicle: Security by Design](https://mlsanity.com/paper/1810.00545/) (2018)
- [Autonomous Vehicles in 5G and Beyond: A Survey](https://mlsanity.com/paper/2207.10510/) (2022)
- [BlockChain: A distributed solution to automotive security and privacy](https://mlsanity.com/paper/1704.00073/) (2017)
- [RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE](https://mlsanity.com/paper/2004.01286/) (2020)
- [A Systematic Survey of Attack Detection and Prevention in Connected and Autonomous Vehicles](https://mlsanity.com/paper/2203.14965/) (2022)
- [Ultra-Reliable Low-Latency Communications in Autonomous Vehicular Networks](https://mlsanity.com/paper/1903.01863/) (2019)
- [Blockchain-enabled Authentication Handover with Efficient Privacy Protection in SDN-based 5G Networks](https://mlsanity.com/paper/1905.03193/) (2019)
- [Model-Assisted Learning for Adaptive Cooperative Perception of Connected Autonomous Vehicles](https://mlsanity.com/paper/2401.10156/) (2024)
- [Vehicular Communications: Survey and Challenges of Channel and Propagation Models](https://mlsanity.com/paper/1505.06004/) (2015)
- [Software-Defined Networks Supporting Time-Sensitive In-Vehicular Communication](https://mlsanity.com/paper/1903.08039/) (2019)
- [B-FERL: Blockchain based Framework for Securing Smart Vehicles](https://mlsanity.com/paper/2007.10528/) (2020)

## Built on by (most cited first, 1 of 1)

- [OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques](https://mlsanity.com/paper/2504.13408/) (2025)

## Abstract (arXiv)

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.