中文摘要：

在车联网中,同时使用多个虚假身份的Sybil攻击,在网络中散布虚假消息,都易造成资源的不公平使用和网络混乱。针对这一问题,提出快速识别车辆虚假位置的事件驱动型轻量级算法,当车辆出现在另一车辆的安全区域内,启动快速识别两车辆是否重叠的几何交叉模型（GCR,geometrical cross-recognition）算法,检测声称虚假位置的错误行为;同时,根据证实车辆收集的邻居范围内的局部车辆,建立位置偏差矩阵（PDM,position deviation matrix）,进一步识别交叉车辆中的Sybil节点。性能分析和仿真实验表明,安全区域驱动下的轻量级算法识别速度快,检测率高,在车辆定位误差较低时性能更好;安全区域的引入也均衡了车辆密度过大时造成的通信负载影响,与同类算法相比,通信处理时延较低。

英文摘要：

In VANET, the Sybil attack simultaneously using multiple forged identities can easily cause the injustice of resource usage and make networks in a mess by distributing false messages. To solve this problem, an event-driven lightweight algorithm was proposed, which could identify vehicles false position quickly. When one vehicle appeared inside another＇s safety zone, a geometrical cross-recognition algorithm to calculate the overlap between vehicles to detect false position claiming was presented. At the same time, according to the neighbors within the confirming vehicle＇s radio range, position deviation matrix was established further to identify the Sybil node of two overlap vehicles. The performance analysis and simulation results show that the lightweight algorithm driven by safety zone demonstrates fast identification and high detection rate, especially when GPS error is very low. The imported safety zone can also balance the communication load impacting by heavy vehicular density. And the communication processing delay is lower than other approaches.