论文标题
一项针对5G和超越车辆网络的基于机器学习的不良检测系统的调查
A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks
论文作者
论文摘要
在部署车辆到所有（V2X）技术方面已取得了重大进展。将V2X与5G集成已实现超低延迟和高可靠性V2X通信。但是，尽管沟通绩效增强了，但安全性和隐私问题已经增加。攻击变得更加积极，攻击者变得更加战略性。标准化机构提出的公共密钥基础设施不能仅仅防御这些攻击。因此，在此补充中，应设计复杂的系统来检测此类攻击和攻击者。机器学习（ML）最近成为确保未来道路的关键推动力。许多V2X不当行为检测系统（MDS）采用了此范式。但是，分析这些系统是一个研究差距，而开发有效的基于ML的MDS仍然是一个空旷的问题。为此，本文介绍了基于ML的MDS的全面调查和分类。我们从安全性和ML的角度分析并讨论它们。然后，我们提供一些有学识的课程和建议，以帮助开发，验证和部署基于ML的MDS。最后，我们通过一些未来的方向重点介绍开放研究和标准化问题。
Significant progress has been made towards deploying Vehicle-to-Everything (V2X) technology. Integrating V2X with 5G has enabled ultra-low latency and high-reliability V2X communications. However, while communication performance has enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure proposed by standardization bodies cannot solely defend against these attacks. Thus, in complementary of that, sophisticated systems should be designed to detect such attacks and attackers. Machine Learning (ML) has recently emerged as a key enabler to secure our future roads. Many V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. Yet, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper present a comprehensive survey and classification of ML-based MDSs. We analyze and discuss them from both security and ML perspectives. Then, we give some learned lessons and recommendations helping in developing, validating, and deploying ML-based MDSs. Finally, we highlight open research and standardization issues with some future directions.
