ReFIoV: A Novel Reputation Framework for Information-Centric Vehicular Applications
ReFIoV: A Novel Reputation Framework for Information-Centric Vehicular Applications
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DOI：
10.1109/tvt.2018.2886572
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发表时间：
2019-02-01
影响因子：
6.8
通讯作者：
Sheng, Zhengguo
中科院分区：
文献类型：
--
作者：
Magaia, Naercio;Sheng, Zhengguo
本文提出了一种利用机器学习和人工免疫系统（AIS）（也称为 ReFIoV）的以信息为中心的车辆应用的新型声誉框架。具体来说，贝叶斯学习和分类允许每个节点在新观察到的其他节点行为数据可用时进行学习，从而对这些节点进行分类，同时，k-means 聚类算法允许我们集成来自其他节点的建议，即使它们的行为方式不可预测。 AIS 用于增强不当行为检测。所提出的 ReFIoV 可以以分布式方式实现，因为每个节点决定与谁交互。它为节点缓存和转发其他人的移动数据提供激励，并实现针对虚假指控和赞扬的鲁棒性。性能评估表明，就所考虑的指标而言，ReFIoV 的性能优于最先进的声誉系统。也就是说，与其他信誉方案相比，它呈现出非常少量的被错误分类的行为不当节点。所提出的 AIS 机制开销较低。建议的纳入使该框架能够进一步减少检测时间。
In this paper, a novel reputation framework for information-centric vehicular applications leveraging on machine learning and the artificial immune system (AIS), also known as ReFIoV, is proposed. Specifically, the Bayesian learning and classification allow each node to learn as newly observed data of the behavior of other nodes become available and hence classify these nodes, meanwhile, the k-means clustering algorithm allows us to integrate recommendations from other nodes even if they behave in an unpredictable manner. The AIS is used to enhance misbehavior detection. The proposed ReFIoV can be implemented in a distributed manner as each node decides with whom to interact. It provides incentives for nodes to cache and forward others' mobile data as well as achieves robustness against false accusations and praise. The performance evaluation shows that ReFIoV outperforms state-of-the-art reputation systems for the metrics considered. That is, it presents a very low number of misbehaving nodes incorrectly classified in comparison with another reputation scheme. The proposed AIS mechanism presents a low overhead. The incorporation of recommendations enabled the framework to reduce even further detection time.