Pearson Correlation Analysis to Detect Misbehavior in VANET
Pearson Correlation Analysis to Detect Misbehavior in VANET
复制标题
用于检测 VANET 中的不当行为的 Pearson 相关分析
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Hong Liu
中科院分区：
文献类型：
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作者：
Prinkle Sharma;J. Petit;Hong Liu
车辆自组织网络（VANET）依靠车辆到车辆和车辆到基础设施的通信来提高道路安全和交通效率。因此，恶意数据可能危及VANET通信的益处。因此，应该在每个车载单元上部署以数据为中心的不当行为检测系统，以提高对接收到的数据的信心。在本文中，我们研究了使用皮尔逊相关检测位置伪造攻击的潜力。分析了四种位置伪造攻击，并讨论了相关矩阵如何检测它们。所提出的解决方案可以实时工作，无需任何培训，但根据道路类型，需要至少4到7秒的历史才能完全有效。实验是在来自怀俄明州联网车辆试点部署和密歇根大学交通研究所的真实的数据集上进行的。
Vehicular Ad-hoc Networks (VANET) rely on Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication to improve road safety and traffic efficiency. Therefore, malicious data could jeopardize the benefits of VANET communication. Hence, a data-centric misbehavior detection system should be deployed on each on-board unit to improve confidence in the received data. In this paper, we investigate the potential of using Pearson Correlation to detect location forging attacks. We analyze four location forging attacks and discuss how the correlation matrix detect them. The proposed solution works in real-time, without any training, but, depending on the type of road, requires at least four to seven seconds of history to be fully effective. Experiments are performed on real datasets from Wyoming Connected Vehicle Pilot Deployment and from University of Michigan Transportation Research Institute.