A Feedback-Driven Lightweight Reputation Scheme for IoV

Rohan Dahiya, Frank Jiang, R. Doss
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引用次数: 2

Abstract

Most applications of Internet of Vehicles (IoVs) rely on collaboration between nodes. Therefore, false information flow in-between these nodes poses the challenging trust issue in rapidly moving IoV nodes. To resolve this issue, a number of mechanisms have been proposed in the literature for the detection of false information and establishment of trust in IoVs, most of which employ reputation scores as one of the important factors. However, it is critical to have a robust and consistent scheme that is suitable to aggregate a reputation score for each node based on the accuracy of the shared information. Such a mechanism has therefore been proposed in this paper. The proposed system utilises the results of any false message detection method to generate and share feedback in the network, this feedback is then collected and filtered to remove potentially malicious feedback in order to produce a dynamic reputation score for each node. The reputation system has been experimentally validated and proved to have high accuracy in the detection of malicious nodes sending false information and is robust or negligibly affected in the presence of spurious feedback.
基于反馈驱动的车联网轻量级信誉方案
大多数车联网应用都依赖于节点之间的协作。因此,在快速移动的车联网节点中,这些节点之间的虚假信息流构成了具有挑战性的信任问题。为了解决这一问题,文献中提出了许多机制来检测虚假信息和建立人工智能的信任,其中大多数都将声誉分数作为重要因素之一。然而,关键是要有一个健壮和一致的方案,适合根据共享信息的准确性为每个节点聚合信誉评分。因此,本文提出了这种机制。所提出的系统利用任何虚假信息检测方法的结果来生成并在网络中共享反馈,然后收集和过滤这些反馈以删除潜在的恶意反馈,以便为每个节点生成动态声誉评分。实验验证了信誉系统在检测发送虚假信息的恶意节点方面具有很高的准确性,并且在存在虚假反馈的情况下具有鲁棒性或可以忽略不计的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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