摘要
针对车联网网络拓扑结构变化快且相比传统移动自组网络更易受到恶意车辆发起的内部攻击等问题,在当前贝叶斯假设的信任理论研究的基础上,结合车联网高速移动中快速检测恶意节点的要求,加大否定事件的影响力度,提出了用于评估车辆节点行为的信任模型;在综合推荐信任值时,引入了"推荐信任距离"作为推荐信任的信任度量,预先排除恶意推荐意见,并有效防止车辆的串通攻击。与现有的基于信任的检测方法相比,该方法加快了检测速度,并简化了推荐传递。仿真实验表明,该方法有较快的检测速度,从网络丢包率和恶意节点检测率可以看出此信任模型对检测恶意节点具有较好的性能。
With the requirement of rapid detection of malicious nodes in high-speed VANET, a trust model was pro- posed on the basis of bayes hypothesis, aiming at solving the issues that the topology of VANET changes quickly and is more vulnerable to malicious internal attack than traditional mobile ad-hoc network. The model intensifies the influence of negative events, to eliminate the malicious recommendations in advance and avoid collusion attack. The concept of ＂recommendation trust distance＂ is introduced as trust metric of recommendation trust when integrating recommenda- tion trust. Compared with the current detection method based on trust, this model speeds up the detection speed and simplifies the recommend delivery. The simulation experiment shows that this trust model has rapid detection speed and good performance in detecting malicious nodes from the network packet loss rate and the detection rate of malicious nodes.
出处
《计算机科学》
CSCD
北大核心
2015年第8期157-160,174,共5页
Computer Science
基金
国家自然科学基金(61272074)
江苏省自然科学基金(BK2011464)
镇江市工业支撑计划项目(GY2013030)资助
关键词
车联网
信任模型
贝叶斯假设
推荐信任距离
网络丢包率
检测率
VANET,Trust model,Bayes hypothesis,Recommendation trust distance,Network packet loss rate,Detection rate
