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

## TL;DR

This paper reviews security challenges in connected and autonomous vehicles related to adversarial machine learning, highlighting vulnerabilities and proposing defense strategies to ensure safety and reliability in future intelligent transportation systems.

## Contribution

It provides a comprehensive overview of ML security issues in CAVs and proposes solutions to defend against adversarial attacks in various scenarios.

## Key findings

- Identification of key security challenges in ML for CAVs
- Analysis of adversarial ML attack vectors in vehicular networks
- Proposed defense mechanisms against adversarial attacks

## Abstract

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.