Vehicular ad hoc network (VANET) is a part of the intelligent transportation system (ITS) that provides safety and nonsafety applications. The high mobility of vehicles and the wireless communication environment in VANET makes it vulnerable to various attacks. One among them is the Sybil attack, where a Sybil attacker creates multiple fake identities called Sybil nodes that disrupt the functionality of VANET. Most of the existing solutions in the literature discuss identifying the Sybil nodes (virtual); very few works exist to determine the Sybil attacker (source node that generates Sybil nodes). In this paper, we propose a computation less heuristic approach that focuses on detecting the Sybil attacker and its Sybil nodes using signal strength measurements and Euclidean distance as the detection parameters. The central VANET server, Road Side Units (RSUs), and vehicles collaborate in the detection process, which improves the accuracy of our approach. The core of the approach is a reward‐based system, where the vehicle rewards are determined by collecting RSUs' feedback about the vehicle behavior. From simulation experiments, it is evident that our proposed approach achieves a maximum detection rate of 99.89% and a false positive rate of 0.012% than the existing techniques.

中文翻译：

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VANET中检测和逐出Sybil攻击者和Sybil节点的协作策略

车载自组织网络（VANET）是提供安全和非安全应用程序的智能运输系统（ITS）的一部分。车辆的高机动性和VANET中的无线通信环境使其容易受到各种攻击。其中之一是Sybil攻击，其中Sybil攻击者创建了多个伪身份，称为Sybil节点，这些身份破坏了VANET的功能。文献中大多数现有的解决方案都讨论了识别Sybil节点（虚拟）的问题。确定Sybil攻击者（生成Sybil节点的源节点）的工作很少。在本文中，我们提出了一种计算量较少的启发式方法，该方法着重于使用信号强度测量和欧几里德距离作为检测参数来检测Sybil攻击者及其Sybil节点。中央VANET服务器，路边单元（RSU）和车辆在检测过程中协作，从而提高了我们方法的准确性。该方法的核心是基于奖励的系统，其中，通过收集RSU关于车辆行为的反馈来确定车辆奖励。从仿真实验可以明显看出，与现有技术相比，我们提出的方法最大检测率达到了99.89％，误报率达到了0.012％。