基于车联网的虚假数据检测模型

摘要

In order to protect the most valuable information of VANET (vehicular Ad-hoc NETwork):vehicle position and velocity,enhance the accuracy and efficiency of the VANET,the security and authenticity of VANET's basic information is studied and a detection model of false data based on vehicular Ad-hoc NETwork is proposed.In this model the vehicle will collect information from different devices includes a peripheral vehicle,wireless network.By cosine similarity clustering method to filter these different sources of traffic information.The experiment show that the error rate is reduced and the accuracy is enhanced,which lay the foundations for position relate applications which use position and velocity.%为了保护最具价值的车联网速度与位置信息,有效提高交通数据的安全性,对车联网VANET (Vehicular Ad-hocNETwork)中基础信息的安全性、真实性进行了研究,提出了一种利用余弦相似度聚类方法的车联网数据真伪检测模型.该模型中车辆将收集到包括周边车辆、无线网络等多方面数据,通过余弦相似度聚类的方法对这些不同来源的交通流数据进行检测、过滤.实验表明,该方法能有效降低信息的错误率,提高安全性,为基于车辆位置、速度基础上的其它应用奠定了很好的基础.