VANET中位置相关的轻量级Sybil攻击检测方法

发布时间：2018-04-30 15:39

  本文选题：Sybil攻击 + 几何交叉模型算法　； 参考：《通信学报》2017年04期

【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。
[Abstract]:In the network of vehicles, using multiple Sybil attacks of false identities at the same time, spreading false messages in the network, it is easy to cause unfair use of resources and network confusion. In order to solve this problem, an event-driven lightweight algorithm is proposed to quickly identify the false position of a vehicle. When the vehicle appears in the safety area of another vehicle, the geometric cross model (GCR geometric cross-recognition) algorithm is started to quickly identify whether the two vehicles overlap. At the same time, according to the local vehicles in the neighborhood range of the confirmed vehicle collection, the position deviation matrix is established to further identify the Sybil nodes in the cross vehicle. The performance analysis and simulation results show that the lightweight algorithm driven by the safe region has the advantages of fast recognition speed, high detection rate and better performance when the vehicle positioning error is low. The introduction of security region also balances the communication load when the vehicle density is too high. Compared with the similar algorithms, the delay of communication processing is lower.
【作者单位】： 江苏大学计算机科学与通信工程学院;安徽大学信息保障技术协同创新中心;
【基金】：国家自然科学基金资助项目(No.61472001) 江苏省重点研发计划基金资助项目(No.BE2015136)~~
【分类号】：U495;TN929.5
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本文编号：1825139