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首页> 外文期刊>Vehicular Communications >Context-aware data-centric misbehaviour detection scheme for vehicular ad hoc networks using sequential analysis of the temporal and spatial correlation of the consistency between the cooperative awareness messages
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Context-aware data-centric misbehaviour detection scheme for vehicular ad hoc networks using sequential analysis of the temporal and spatial correlation of the consistency between the cooperative awareness messages

机译：使用协作意识消息之间一致性的时间和空间相关性的顺序分析，用于车辆自组织网络的上下文感知的以数据为中心的不良行为检测方案

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摘要

Vehicular ad hoc Networks (VANETs) have emerged mainly to improve road safety, traffic efficiency, and passenger comfort. The performance of most VANET applications and services relies on the availability of accurate and up-to-date mobility-information, through so-called Cooperative Awareness Messages (CAMs), shared by neighbouring vehicles. However, sharing false mobility information can disrupt any potential VANET application. As cryptographic techniques used to protect CAMs in VANETs are expensive, complicated, and vulnerable to internal misbehaviour, security lapses are inevitable. Although several misbehaviour detection solutions have been proposed, those solutions assume that the VANET's context is stationary, which does not hold for VANETs in a real scenario, as the vehicle's context changes continuously. The use of static and predefined security thresholds in highly dynamic and harsh environments is the major drawback of those solutions. To address this issue, a context-aware data-centric misbehaviour detection scheme (CA-DC-MDS) is proposed, using sequential analysis of temporal and spatial correlation of the consistency between neighbouring vehicles' mobility information. The static thresholds have been replaced by a dynamic context reference model that is constructed online and updated in a timely fashion using statistical techniques. Firstly, the Kalman filter algorithm is used to track the mobility information received from neighbouring vehicles. Then, the innovation errors of the Kalman filter are utilized to construct a temporal consistency assessment model for each neighbouring vehicle, using a box-and-whisker plot. After that, the Hampel filter is used to construct a spatial consistency assessment model that represents the current context reference. Similarly, plausibility assessment reference models are built online and updated in a timely fashion using the Hampel filter and by utilizing the consistency assessment reference model of neighbouring information. Finally, a message is classified as suspicious if its consistency and plausibility scores deviate significantly from the context reference model. The proposed context-aware scheme achieved a 73% reduction in the false alarm rate while achieving a 37% improvement in the detection rate. This demonstrates the effectiveness of the proposed context-aware scheme compared with the existing static solutions. (c) 2019 Elsevier Inc. All rights reserved.
机译：车载自组织网络（VANET）的出现主要是为了提高道路安全性，交通效率和乘客舒适度。大多数VANET应用程序和服务的性能依赖于相邻车辆共享的所谓合作意识消息（CAM）来获得准确和最新的移动性信息。但是，共享错误的移动性信息可能会破坏任何潜在的VANET应用程序。由于用于保护VANET中的CAM的加密技术昂贵，复杂且易受内部不当行为的影响，因此不可避免地会出现安全漏洞。尽管已经提出了几种不良行为检测解决方案，但是这些解决方案都假定VANET的上下文是固定的，在实际情况下，VANET并不适用，因为车辆的上下文不断变化。这些解决方案的主要缺点是在高度动态和恶劣的环境中使用静态和预定义的安全阈值。为了解决这个问题，提出了一种基于上下文的以数据为中心的不当行为检测方案（CA-DC-MDS），该方法通过对相邻车辆的移动性信息之间一致性的时间和空间相关性进行顺序分析。静态阈值已由动态上下文参考模型代替，该模型在线构建并使用统计技术及时更新。首先，卡尔曼滤波算法用于跟踪从相邻车辆接收的移动性信息。然后，利用箱须图，利用卡尔曼滤波器的创新误差为每个相邻车辆构建时间一致性评估模型。之后，使用Hampel滤波器构建代表当前上下文参考的空间一致性评估模型。类似地，在线建立合理性评估参考模型，并使用Hampel过滤器并利用邻近信息的一致性评估参考模型及时更新。最后，如果一条消息的一致性和合理性得分明显偏离上下文参考模型，则将其分类为可疑消息。所提出的情境感知方案将误报率降低了73％，同时将检测率提高了37％。与现有的静态解决方案相比，这证明了所提出的上下文感知方案的有效性。 （c）2019 Elsevier Inc.保留所有权利。

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