基于弱分类器集成的车联网虚假交通信息检测

False traffic information detection based on weak classifiers integration in vehicular ad hoc networks

车联网中车辆以自组织的方式相互报告交通信息,开放的网络环境需要甄别消息,然而,要快速移动的车辆在短时间内检测出大量的交通警报信息是非常困难的。针对这一问题,提出一种基于弱分类器集成的虚假交通信息检测方法。首先,扩充交通警报信息的有效特征,并设计分割规则,将信息的特征集划分为多个特征子集;然后,根据子集特征的不同特性,使用对应的弱分类器分别进行处理。仿真实验和性能分析表明,选用弱分类器集成方法检测车联网中的虚假交通信息减少了检测时间,且由于综合特征的应用,检测率优于仅使用部分特征的检测结果。

Vehicles report traffic information mutually by self-organized manner in vehicular ad hoc networks （VANET）, and the message need to be identified in the open network environment. However, it is very difficult for fast moving vehicles to detect a lot of traffic alert information in a short time. To solve this problem, a false traffic message detection method was presented based on weak classifiers integration. Firstly, the effective features of traffic alert information was extended and segmentation rules were designed to divide the information feature set into multiple feature subsets, then the corresponding weak classifiers were used to process feature subsets respectively according to the different character- istics of the subsets＇ features. Simulation experiments and performance analysis show that the selected weak classifiers integration method reduces the detection time, and because of the application of combined features, the detection rate is better than the test of using only some of the characteristics.