基于弱分类器集成的车联网虚假交通信息检测

doi:10.11959/j.issn.1000-436x.2016156

Abstract

Abstract:

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 ve-hicles 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.

Key words: VANET, false information detection, weak classifiers integration, BP neural network

Xiang-wen LIU,Ya-li SHI,ENGXia F. False traffic information detection based on weak classifiers integration in vehicular ad hoc networks[J]. Journal on Communications, 2016, 37(8): 58-66.

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References 17

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方法	基于节点特征的方法		基于信息特征的方法
方法	信任机制	投票机制	分类器优化	多源特征
优点	检测简单，开销小	能测出有历史行为的恶意节点	信息特征越多，检测精度越高	信息源越多，检测精度越高
缺点	受恶意节点的信任值影响	受恶意节点比例影响	检测复杂度高	依赖于环境因素
综合分析	检测简单，适用于车联网环境；依赖于通信环境，对消息判断往往不准确		信息特征和信息源越多，检测率越高；检测复杂度较高、警报信息较多时，时延会损害警报信息的时效性

序号	特征名称
1	Sender (s)
2	Priority (p)
3	Occur time (t₀)
4	Occur site (l₀)
5	Trans time (t_s)
6	Trans site (l_s)
7	Direction (d_s)
8	Vehicle vel (v_s)
9	Vehicle acc (a_s)
10	Reputation (r_s)
11	Retransmit (n)

特征子集	子集名称	子集内容
特征子集1	事件紧急度	t₀,l₀,p
特征子集2	道路状况	t_s,l_s,v_s,a_s,d_s
特征子集3	信息可信度	r_s,n

仿真参数	参数值
仿真时间	1 000 s
仿真场景范围	1 000 m×1 000 m
道路数量	Random
车辆数目	100辆
车辆速度	10~30 m/s
移动录制时间步长	0.05 s

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

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