Zhu Yaling, Zeng Jia, Weng Fangchen, Han Dan, Yang Yiyu, Li Xiaoqi, Zhang Yuqing
The School of Cyberspace Security, Hainan University, Haikou 570208, China.
The National Computer Intrusion Protection Center, University of Chinese Academy of Sciences, Beijing 101408, China.
Sensors (Basel). 2024 Mar 27;24(7):2153. doi: 10.3390/s24072153.
Connected Automobile Vehicles (CAVs) enable cooperative driving and traffic management by sharing traffic information between them and other vehicles and infrastructures. However, malicious vehicles create Sybil vehicles by forging multiple identities and sharing false location information with CAVs, misleading their decisions and behaviors. The existing work on defending against Sybil attacks has almost exclusively focused on detecting Sybil vehicles, ignoring the traceability of malicious vehicles. As a result, they cannot fundamentally alleviate Sybil attacks. In this work, we focus on tracking the attack source of malicious vehicles by using a novel detection mechanism that relies on vehicle broadcast beacon packets. Firstly, the roadside units (RSUs) randomly instruct vehicles to perform customized key broadcasting and listening within communication range. This allows the vehicle to prove its physical presence by broadcasting. Then, RSU analyzes the beacon packets listened to by the vehicle and constructs a neighbor graph between the vehicles based on the customized particular fields in the beacon packets. Finally, the vehicle's credibility is determined by calculating the edge success probability of vehicles in the neighbor graph, ultimately achieving the detection of Sybil vehicles and tracing malicious vehicles. The experimental results demonstrate that our scheme achieves the real-time detection and tracking of Sybil vehicles, with precision and recall rates of 98.53% and 95.93%, respectively, solving the challenge of existing detection schemes failing to combat Sybil attacks from the root.
联网汽车（CAV）通过在它们自身以及与其他车辆和基础设施之间共享交通信息，实现协同驾驶和交通管理。然而，恶意车辆通过伪造多个身份并与联网汽车共享虚假位置信息来创建虚拟身份车辆，误导它们的决策和行为。现有的抵御虚拟身份攻击的工作几乎都只专注于检测虚拟身份车辆，而忽略了恶意车辆的可追溯性。因此，它们无法从根本上缓解虚拟身份攻击。在这项工作中，我们专注于通过使用一种依赖车辆广播信标数据包的新型检测机制来追踪恶意车辆的攻击源。首先，路边单元（RSU）随机指示车辆在通信范围内执行定制的密钥广播和监听。这使得车辆能够通过广播来证明其物理存在。然后，RSU分析车辆监听的信标数据包，并根据信标数据包中的定制特定字段构建车辆之间的邻居图。最后，通过计算邻居图中车辆的边成功概率来确定车辆的可信度，最终实现对虚拟身份车辆的检测并追踪恶意车辆。实验结果表明，我们的方案实现了对虚拟身份车辆的实时检测和追踪，精确率和召回率分别为98.53%和95.93%，解决了现有检测方案无法从根本上对抗虚拟身份攻击的挑战。
