Cybersecurity Challenges in the EV Charging Ecosystem

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Amanjot Kaur, Nima Valizadeh, Devki Nandan, Tomasz Szydlo, James R. K. Rajasekaran, Vijay Kumar, Mutaz Barika, Jun Liang, Rajiv Ranjan, Rana Omer
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引用次数: 0

Abstract

The growing adoption of intelligent Electric Vehicles (EVs) has also created an opportunity for malicious actors to initiate attacks on the EV infrastructure, which can include a number of data exchange protocols across the various entities that are part of the EV charging ecosystem. These protocols possess a range of underlying vulnerabilities that attackers can exploit to disrupt the regular flow of information and energy. While researchers have considered vulnerabilities of particular components within an EV charging ecosystem, there is still a notable gap in vulnerability analysis of charging protocols and the potential threats to these. We investigate threat vectors within the most widely adopted protocols used in EV infrastructure, explore the potential impact of cyberattacks and suggest various mitigation techniques investigated in literature. Potential future research directions are also identified.
电动汽车充电生态系统中的网络安全挑战
智能电动汽车(EV)的日益普及也为恶意行为者发起对电动汽车基础设施的攻击创造了机会,其中可能包括作为电动汽车充电生态系统一部分的各个实体之间的许多数据交换协议。这些协议具有一系列潜在的漏洞,攻击者可以利用这些漏洞来破坏正常的信息和能量流。虽然研究人员已经考虑了电动汽车充电生态系统中特定组件的漏洞,但在充电协议的漏洞分析及其潜在威胁方面仍然存在显著差距。我们研究了电动汽车基础设施中使用的最广泛采用的协议中的威胁向量,探索了网络攻击的潜在影响,并提出了文献中研究的各种缓解技术。并指出了未来可能的研究方向。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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