汽车众包中防范Sybils的新型招聘策略

Federico Concone, Fabrizio De Vita, A. Pratap, Dario Bruneo, G. Re, Sajal K. Das
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引用次数: 0

摘要

车载社交网络(VSNs)是一种新兴的通信模式,它融合了在线社交网络(OSNs)和车载自组织网络(VANETs)的概念。由于缺乏健壮的身份验证机制,基于社交的车辆应用程序容易受到许多攻击,包括网络中生成符号实体。我们在车辆众包活动中解决了这个重要问题,在这些活动中,黑客通常被用来增加他们的影响力,并使系统的功能恶化。特别是,我们提出了一种新颖的用户招募策略(URP),该策略在提取众包活动事件半径内的参与者后,使用一种称为SybilDriver的新颖的SybilDriver检测并过滤出sybiler车辆。该技术结合了vanet和OSN的优点,通过从物理车辆网络中获得的接近图的创新概念,并结合了OSN领域采用的社区检测和随机森林技术。详细的实验评估证明了我们的方法的有效性,并且还表明它优于osn中通常使用的现有最先进的方法
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Recruitment Policy to Defend against Sybils in Vehicular Crowdsourcing
Vehicular Social Networks (VSNs) is an emerging communication paradigm, derived by merging the concepts of Online Social Networks (OSNs) and Vehicular Ad-hoc Networks (VANETs). Due to the lack of robust authentication mechanisms, social-based vehicular applications are vulnerable to numerous attacks including the generation of sybil entities in the networks. We address this important issue in vehicular crowdsourcing campaigns where sybils are usually employed to increase their influence and worsen the functioning of the system. In particular, we propose a novel User Recruitment Policy (URP) that, after extracting the participants within the event radius of a crowdsourcing campaign, detects and filters out the sybil vehicles by using a novel sybil detection approach, called SybilDriver. This technique combines the advantages of VANETs and OSNs by means of an innovative concept of proximity graph obtained from the physical vehicular network, in conjunction with a community detection and Random Forest techniques adopted in the OSN domain. Detailed experimental evaluations demonstrate the effectiveness of our approach and also show that it outperforms existing state-of-the-art methods typically used in the OSNs.1
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