Cybersecurity for Autonomous Vehicles

• URL: http://arxiv.org/abs/2504.20180v1
• Date: Mon, 28 Apr 2025 18:31:37 GMT
• Title: Cybersecurity for Autonomous Vehicles
• Authors: Sai varun reddy Bhemavarapu,
• Abstract summary: The increasing adoption of autonomous vehicles is bringing a major shift in the automotive industry.<n>As these vehicles become more connected, cybersecurity threats have emerged as a serious concern.<n>This paper discusses major cybersecurity challenges like vulnerabilities in software and hardware, risks from wireless communication, and threats through external interfaces.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The increasing adoption of autonomous vehicles is bringing a major shift in the automotive industry. However, as these vehicles become more connected, cybersecurity threats have emerged as a serious concern. Protecting the security and integrity of autonomous systems is essential to prevent malicious activities that can harm passengers, other road users, and the overall transportation network. This paper focuses on addressing the cybersecurity issues in autonomous vehicles by examining the challenges and risks involved, which are important for building a secure future. Since autonomous vehicles depend on the communication between sensors, artificial intelligence, external infrastructure, and other systems, they are exposed to different types of cyber threats. A cybersecurity breach in an autonomous vehicle can cause serious problems, including a loss of public trust and safety. Therefore, it is very important to develop and apply strong cybersecurity measures to support the growth and acceptance of self-driving cars. This paper discusses major cybersecurity challenges like vulnerabilities in software and hardware, risks from wireless communication, and threats through external interfaces. It also reviews existing solutions such as secure software development, intrusion detection systems, cryptographic protocols, and anomaly detection methods. Additionally, the paper highlights the role of regulatory bodies, industry collaborations, and cybersecurity standards in creating a secure environment for autonomous vehicles. Setting clear rules and best practices is necessary for consistent protection across manufacturers and regions. By analyzing the current cybersecurity landscape and suggesting practical countermeasures, this paper aims to contribute to the safe development and public trust of autonomous vehicle technology.

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)
• Security by Design Issues in Autonomous Vehicles [0.7999703756441756]
  This research outlines the diverse security layers, spanning physical, cyber, coding, and communication aspects, in the context of AVs.<n>We provide insights into potential solutions for each potential attack vector, ensuring that autonomous vehicles remain secure and resilient in an evolving threat landscape.
  arXiv  Detail & Related papers  (2025-01-07T19:24:11Z)
• Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection [0.0]
  The automotive industry increasingly adopts connected and automated vehicles (CAVs)<n>With the emergence of new vulnerabilities and security requirements, the integration of advanced technologies presents promising avenues for enhancing CAV cybersecurity.<n>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)
• Navigating the road to automotive cybersecurity compliance [39.79758414095764]
  The automotive industry is compelled to adopt robust cybersecurity measures to safeguard both vehicles and data against potential threats. The future of automotive cybersecurity lies in the continuous development of advanced protective measures and collaborative efforts among all stakeholders.
  arXiv  Detail & Related papers  (2024-06-29T16:07:48Z)
• 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)
• Physical Backdoor Attack can Jeopardize Driving with Vision-Large-Language Models [53.701148276912406]
  Vision-Large-Language-models (VLMs) have great application prospects in autonomous driving. BadVLMDriver is the first backdoor attack against VLMs for autonomous driving that can be launched in practice using physical objects. BadVLMDriver achieves a 92% attack success rate in inducing a sudden acceleration when coming across a pedestrian holding a red balloon.
  arXiv  Detail & Related papers  (2024-04-19T14:40:38Z)
• Autonomous Vehicles an overview on system, cyber security, risks, issues, and a way forward [0.0]
  This chapter explores the complex realm of autonomous cars, analyzing their fundamental components and operational characteristics. The primary focus of this investigation lies in the realm of cybersecurity, specifically in the context of autonomous vehicles. A comprehensive analysis will be conducted to explore various risk management solutions aimed at protecting these vehicles from potential threats.
  arXiv  Detail & Related papers  (2023-09-25T15:19:09Z)
• When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
  We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures. We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN. Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
  arXiv  Detail & Related papers  (2023-06-09T14:33:26Z)
• Roadmap for Cybersecurity in Autonomous Vehicles [3.577310844634503]
  We discuss major automotive cyber-attacks over the past decade and present state-of-the-art solutions that leverage artificial intelligence (AI) We propose a roadmap towards building secure autonomous vehicles and highlight key open challenges that need to be addressed.
  arXiv  Detail & Related papers  (2022-01-19T16:42:18Z)
• 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.