EVScout2.0: Electric Vehicle Profiling Through Charging Profile

IF 2 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS ACM Transactions on Cyber-Physical Systems Pub Date : 2021-06-30 DOI:10.1145/3565268
Alessandro Brighente, M. Conti, Denis Donadel, F. Turrin
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引用次数: 9

Abstract

Electric Vehicles (EVs) represent a green alternative to traditional fuel-powered vehicles. To enforce their widespread use, both the technical development and the security of users shall be guaranteed. Users’ privacy represents a possible threat that impairs the adoption of EVs. In particular, recent works showed the feasibility of identifying EVs based on the current exchanged during the charging phase. In fact, while the resource negotiation phase runs over secure communication protocols, the signal exchanged during the actual charging contains features peculiar to each EV. In what is commonly known as profiling, a suitable feature extractor can associate such features to each EV. In this paper, we propose EVScout2.0, an extended and improved version of our previously proposed framework to profile EVs based on their charging behavior. By exploiting the current and pilot signals exchanged during the charging phase, our scheme can extract features peculiar for each EV, hence allowing their profiling. We implemented and tested EVScout2.0 over a set of real-world measurements considering over 7500 charging sessions from a total of 137 EVs. In particular, numerical results show the superiority of EVScout2.0 with respect to the previous version. EVScout2.0 can profile EVs, attaining a maximum of 0.88 for both recall and precision scores in the case of a balanced dataset. To the best of the authors’ knowledge, these results set a new benchmark for upcoming privacy research for large datasets of EVs.
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EVScout2.0:通过充电配置文件分析电动汽车
电动汽车(EV)代表了传统燃料动力汽车的绿色替代品。为了使其广泛使用,应保证技术发展和用户的安全。用户的隐私可能会威胁到电动汽车的普及。特别是,最近的工作表明了基于充电阶段交换的电流来识别电动汽车的可行性。事实上,虽然资源协商阶段通过安全通信协议运行,但在实际充电过程中交换的信号包含每个电动汽车特有的特征。在通常称为评测的过程中,合适的特征提取器可以将这些特征与每个电动汽车相关联。在本文中,我们提出了EVScout2.0,我们之前提出的基于电动汽车充电行为对其进行评测的框架的扩展和改进版本。通过利用充电阶段交换的电流和导频信号,我们的方案可以提取每辆电动汽车特有的特征,从而允许对其进行分析。我们在一组真实世界的测量中实施并测试了EVScout2.0,考虑到总共137辆电动汽车的7500多个充电会话。特别是,数值结果显示了EVScout2.0相对于先前版本的优越性。EVScout2.0可以评测电动汽车,在平衡数据集的情况下,召回率和准确率得分最高可达0.88。据作者所知,这些结果为即将进行的电动汽车大型数据集隐私研究树立了新的基准。
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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来源期刊
ACM Transactions on Cyber-Physical Systems
ACM Transactions on Cyber-Physical Systems COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
5.70
自引率
4.30%
发文量
40
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