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

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.

References in corpus (10)

• Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
• One pixel attack for fooling deep neural networks
• Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
• Deep Reinforcement Learning framework for Autonomous Driving
• DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
• TextBugger: Generating Adversarial Text Against Real-world Applications
• DLFuzz: Differential Fuzzing Testing of Deep Learning Systems
• Toward Intelligent Vehicular Networks: A Machine Learning Framework
• Towards Proving the Adversarial Robustness of Deep Neural Networks
• Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection

Cited by in corpus (8)

• Machine Learning for Security in Vehicular Networks: A Comprehensive Survey
• 6G: The Intelligent Network of Everything
• Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses
• Safeguarding connected autonomous vehicle communication: Protocols, intra- and inter-vehicular attacks and defenses
• Controlling Chaos Using Edge Computing Hardware
• Deep learning for nano-photonic materials -- The solution to everything!?
• Active Poisoning: Efficient Backdoor Attacks on Transfer Learning-Based Brain-Computer Interfaces
• DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder