# Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward

arXiv: https://arxiv.org/abs/1905.12762 · HTML: https://mlsanity.com/paper/1905.12762/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2019.

## Built on by (most cited first, 30 of 33)

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- [SoK: On the Semantic AI Security in Autonomous Driving](https://mlsanity.com/paper/2203.05314/) (2022)
- [Resource Constrained Vehicular Edge Federated Learning with Highly Mobile Connected Vehicles](https://mlsanity.com/paper/2210.15496/) (2022)
- [Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples](https://mlsanity.com/paper/2102.04150/) (2021)
- [A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks](https://mlsanity.com/paper/2201.10500/) (2022)
- [Controlling Chaos Using Edge Computing Hardware](https://mlsanity.com/paper/2406.12876/) (2024)
- [Parallel and Multi-Objective Falsification with Scenic and VerifAI](https://mlsanity.com/paper/2107.04164/) (2021)
- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [Game Theoretic Mixed Experts for Combinational Adversarial Machine Learning](https://mlsanity.com/paper/2211.14669/) (2022)
- [Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally](https://mlsanity.com/paper/2310.03614/) (2023)
- [Roadmap for Cybersecurity in Autonomous Vehicles](https://mlsanity.com/paper/2201.10349/) (2022)
- [Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI](https://mlsanity.com/paper/2410.09043/) (2024)
- [Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses](https://mlsanity.com/paper/2210.13289/) (2022)
- [Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey](https://mlsanity.com/paper/2310.10665/) (2023)
- [Explaining RADAR features for detecting spoofing attacks in Connected Autonomous Vehicles](https://mlsanity.com/paper/2203.00150/) (2022)
- [Security of Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2212.06123/) (2022)
- [Conformalized Semi-supervised Random Forest for Classification and Abnormality Detection](https://mlsanity.com/paper/2302.02237/) (2023)
- [Can We Revitalize Interventional Healthcare with AI-XR Surgical Metaverses?](https://mlsanity.com/paper/2304.00007/) (2023)
- [DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder](https://mlsanity.com/paper/2111.03480/) (2021)
- [Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial Robustness](https://mlsanity.com/paper/2202.07342/) (2022)
- [VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle Robustness](https://mlsanity.com/paper/2409.12997/) (2024)
- [Deceptive Sequential Decision-Making via Regularized Policy Optimization](https://mlsanity.com/paper/2501.18803/) (2025)
- [Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses](https://mlsanity.com/paper/2502.04201/) (2025)
- [GAN-Based Single-Stage Defense for Traffic Sign Classification Under Adversarial Patch](https://mlsanity.com/paper/2503.12567/) (2025)
- [Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems](https://mlsanity.com/paper/2207.04843/) (2022)
- [Potential Auto-driving Threat: Universal Rain-removal Attack](https://mlsanity.com/paper/2211.09959/) (2022)
- [A V2X-based Privacy Preserving Federated Measuring and Learning System](https://mlsanity.com/paper/2401.13848/) (2024)
- [Impact of White-Box Adversarial Attacks on Convolutional Neural Networks](https://mlsanity.com/paper/2410.02043/) (2024)

## Abstract (arXiv)

Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services. Such a transformation---which will be fuelled by concomitant advances in technologies for machine learning (ML) and wireless communications---will enable a future vehicular ecosystem that is better featured and more efficient. However, there are lurking security problems related to the use of ML in such a critical setting where an incorrect ML decision may not only be a nuisance but can lead to loss of precious lives. In this paper, we present an in-depth overview of the various challenges associated with the application of ML in vehicular networks. In addition, we formulate the ML pipeline of CAVs and present various potential security issues associated with the adoption of ML methods. In particular, we focus on the perspective of adversarial ML attacks on CAVs and outline a solution to defend against adversarial attacks in multiple settings.