The utilization of autonomous vehicles is experiencing a rapid proliferation in contemporary society. Concurrently, with the relentless evolution of technology, the inexorable integration of autonomous vehicles into urban environments, driven by the overarching paradigm of smart cities, becomes increasingly apparent. This escalating reliance on autonomous vehicles concurrently heightens the susceptibility to malevolent actors orchestrating cyber-attacks against these vehicular systems. While previous years have seen a limited corpus of academic research pertaining to cyber-attack and defense methodologies for autonomous vehicles, the relentless progression of technology mandates a more contemporary and exhaustive inquiry. In addition, to the best of our knowledge, there is no article in the literature that provides detailed information and comparisons about in-vehicle sensors, in-vehicle networks, and in-vehicle network datasets by combining them in one article. Also, to our knowledge, very limited studies have been conducted on separately comparative analysis of in-vehicle networks, in-vehicle sensors or data sets in 2024, and therefore, the necessity of conducting a review study on these topics was recognized. To address this deficiency, we compile articles on attacks and defenses on sensors, in-vehicle networks and present detailed information about the latest datasets and provide comparative analysis. In this paper, we have analyzed 108 papers from the last 10 years on in-vehicle networks and sensors. 38 articles on in-vehicle sensors and 70 articles on in-vehicle networks were reviewed and analyzed. We categorize in-vehicle communication attacks into two main groups: sensor-initiated and network-initiated, with a chronological classification to highlight their evolution. We also compare the progress in securing in-vehicle communication and evaluate the most widely used datasets for attack and protection methods. Additionally, we discuss the advantages and disadvantages of these datasets and suggest future research directions. To the best of our knowledge, this work is the first to offer detailed information and comparative analysis of in-vehicle networks, sensors, and the latest datasets. While the study highlights the significant research conducted to protect in-vehicle networks and sensors from cyber attacks, technological advancements continue to introduce new attack vectors. Cars remain particularly susceptible to threats such as DoS, Fuzzy, Spoofing, and Replay attacks. Moreover, current defense mechanisms, including LSTM and CNN, have notable limitations. Future research is needed to address these challenges and enhance vehicle cybersecurity.

中文翻译：

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车载通信网络安全：挑战和解决方案的全面回顾


自动驾驶汽车的使用在当代社会正在迅速普及。与此同时，随着技术的不断发展，在智慧城市总体范式的推动下，自动驾驶汽车不可阻挡地融入城市环境变得越来越明显。这种对自动驾驶汽车的日益依赖同时增加了对恶意行为者策划针对这些车辆系统的网络攻击的敏感性。虽然前几年与自动驾驶汽车的网络攻击和防御方法相关的学术研究语料库有限，但技术的不断进步要求进行更现代、更详尽的调查。此外，据我们所知，文献中没有一篇文章通过将车载传感器、车载网络和车载网络数据集合并到一篇文章中来提供有关它们的详细信息和比较。此外，据我们所知，到 2024 年，对车载网络、车载传感器或数据集的单独比较分析进行的研究非常有限，因此，人们认识到对这些主题进行审查研究的必要性。为了解决这一缺陷，我们汇编了有关传感器、车载网络攻击和防御的文章，并提供了有关最新数据集的详细信息并提供了比较分析。在本文中，我们分析了过去 10 年中关于车载网络和传感器的 108 篇论文。综述和分析了 38 篇关于车载传感器的文章和 70 篇关于车载网络的文章。 我们将车载通信攻击分为两大类：传感器发起和网络发起，并按时间顺序分类以突出其演变。我们还比较了保护车内通信的进展，并评估了最广泛使用的攻击和保护方法数据集。此外，我们还讨论了这些数据集的优缺点，并提出了未来的研究方向。据我们所知，这项工作首次提供了车载网络、传感器和最新数据集的详细信息和比较分析。虽然该研究强调了为保护车载网络和传感器免受网络攻击而进行的重要研究，但技术进步不断引入新的攻击媒介。汽车仍然特别容易受到 DoS、Fuzzy、欺骗和 Replay 攻击等威胁。此外，当前的防御机制（包括 LSTM 和 CNN）具有明显的局限性。需要未来的研究来应对这些挑战并增强车辆网络安全。