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汽车网络安全:关于框架、标准以及测试与监测技术的综述

Automotive Cybersecurity: A Survey on Frameworks, Standards, and Testing and Monitoring Technologies.

作者信息

Kifor Claudiu Vasile, Popescu Aurelian

机构信息

Faculty of Engineering, Lucian Blaga University of Sibiu, 55024 Sibiu, Romania.

出版信息

Sensors (Basel). 2024 Sep 23;24(18):6139. doi: 10.3390/s24186139.

DOI:10.3390/s24186139
PMID:39338883
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11435732/
Abstract

Modern vehicles are increasingly interconnected through various communication channels, which requires secure access for authorized users, the protection of driver assistance and autonomous driving system data, and the assurance of data integrity against misuse or manipulation. While these advancements offer numerous benefits, recent years have exposed many intrusion incidents, revealing vulnerabilities and weaknesses in current systems. To sustain and enhance the performance, quality, and reliability of vehicle systems, software engineers face significant challenges, including in diverse communication channels, software integration, complex testing, compatibility, core reusability, safety and reliability assurance, data privacy, and software security. Addressing cybersecurity risks presents a substantial challenge in finding practical solutions to these issues. This study aims to analyze the current state of research regarding automotive cybersecurity, with a particular focus on four main themes: frameworks and technologies, standards and regulations, monitoring and vulnerability management, and testing and validation. This paper highlights key findings, identifies existing research gaps, and proposes directions for future research that will be useful for both researchers and practitioners.

摘要

现代车辆通过各种通信渠道越来越多地相互连接,这要求为授权用户提供安全访问,保护驾驶员辅助和自动驾驶系统数据,并确保数据完整性以防滥用或操纵。虽然这些进步带来了诸多好处,但近年来暴露了许多入侵事件,揭示了当前系统中的漏洞和弱点。为了维持和提高车辆系统的性能、质量和可靠性,软件工程师面临着重大挑战,包括在不同的通信渠道、软件集成、复杂测试、兼容性、核心可重用性、安全和可靠性保证、数据隐私以及软件安全等方面。应对网络安全风险在为这些问题找到切实可行的解决方案方面构成了重大挑战。本研究旨在分析汽车网络安全的当前研究状况,特别关注四个主要主题:框架和技术、标准和法规、监测和漏洞管理以及测试和验证。本文突出了关键发现,确定了现有研究差距,并提出了未来研究方向,这对研究人员和从业人员都将是有用的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36cd/11435732/7f15616780ee/sensors-24-06139-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36cd/11435732/5c1814e8dbd1/sensors-24-06139-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36cd/11435732/fabe56cc95fe/sensors-24-06139-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36cd/11435732/7f15616780ee/sensors-24-06139-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36cd/11435732/5c1814e8dbd1/sensors-24-06139-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36cd/11435732/fabe56cc95fe/sensors-24-06139-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/36cd/11435732/7f15616780ee/sensors-24-06139-g003.jpg

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