Security by Design Issues in Autonomous Vehicles

• URL: http://arxiv.org/abs/2501.04104v1
• Date: Tue, 07 Jan 2025 19:24:11 GMT
• Title: Security by Design Issues in Autonomous Vehicles
• Authors: Martin Higgins, Devki Jha, David Blundell, David Wallom,
• Abstract summary: This research outlines the diverse security layers, spanning physical, cyber, coding, and communication aspects, in the context of AVs. We provide insights into potential solutions for each potential attack vector, ensuring that autonomous vehicles remain secure and resilient in an evolving threat landscape.
• Score: 0.7999703756441756
• License:
• Abstract: As autonomous vehicle (AV) technology advances towards maturity, it becomes imperative to examine the security vulnerabilities within these cyber-physical systems. While conventional cyber-security concerns are often at the forefront of discussions, it is essential to get deeper into the various layers of vulnerability that are often overlooked within mainstream frameworks. Our goal is to spotlight imminent challenges faced by AV operators and explore emerging technologies for comprehensive solutions. This research outlines the diverse security layers, spanning physical, cyber, coding, and communication aspects, in the context of AVs. Furthermore, we provide insights into potential solutions for each potential attack vector, ensuring that autonomous vehicles remain secure and resilient in an evolving threat landscape.

Related papers

• Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
  Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks. In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
  arXiv  Detail & Related papers  (2025-01-09T03:59:10Z)
• Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection [0.0]
  The automotive industry increasingly adopts connected and automated vehicles (CAVs) With the emergence of new vulnerabilities and security requirements, the integration of advanced technologies presents promising avenues for enhancing CAV cybersecurity. The roadmap for cybersecurity in autonomous vehicles emphasizes the importance of efficient intrusion detection systems and AI-based techniques.
  arXiv  Detail & Related papers  (2024-12-31T04:08:42Z)
• A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles [0.0]
  This survey focuses on the intersection of Adversarial Machine Learning (AML) and autonomous systems. We comprehensively explore the threat landscape, encompassing cyber-attacks on sensors and adversarial perturbations. This paper endeavors to present a concise overview of the challenges and advances in securing autonomous driving systems against adversarial threats.
  arXiv  Detail & Related papers  (2024-11-21T01:26:52Z)
• Securing Tomorrow's Smart Cities: Investigating Software Security in Internet of Vehicles and Deep Learning Technologies [1.0377683220196872]
  Integrating Deep Learning (DL) techniques in the Internet of Vehicles (IoV) introduces many security challenges and issues that require thorough examination. This literature review delves into the inherent vulnerabilities and risks associated with DL in IoV systems, shedding light on the multifaceted nature of security threats.
  arXiv  Detail & Related papers  (2024-07-23T11:56:33Z)
• Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers? [60.51287814584477]
  This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs. We develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios.
  arXiv  Detail & Related papers  (2024-05-14T09:42:21Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• Survey on Security Attacks in Connected and Autonomous Vehicular Systems [0.0]
  This study provides a brief review of the state of cyber security in the CAVs environment. It categorizes the multiple cybersecurity threats and weaknesses in the context of CAVs into three groups: attacks on the vehicles network, attacks on the Internet at large, and other attacks. It details the most uptodate defense tactics for securing CAVs and analyzes how effective they are.
  arXiv  Detail & Related papers  (2023-10-14T06:37:05Z)
• A Survey of Security in UAVs and FANETs: Issues, Threats, Analysis of Attacks, and Solutions [1.0923877073891446]
  It is critical that security is ensured for UAVs and the networks that provide communication between UAVs. This survey seeks to provide a comprehensive perspective on security within the domain of UAVs and Flying Ad Hoc Networks (FANETs)
  arXiv  Detail & Related papers  (2023-06-25T16:15:40Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.