防止车队重放攻击的假名跟踪无证书聚合签名方案

Yunpeng Zhang, Daniel Egwede, Guohui Zhang, Xuqing Wu
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引用次数: 1

摘要

协作式自适应巡航控制(Cooperative Adaptive Cruise Control, CACC)是一项乐观的创新,它允许联网自动驾驶车辆在先进的交通管理系统中以分布式的方式与其他联网车辆共享参数,从而提高驾驶安全性和道路容量,降低能耗。然而,由于其技术的互联性,CACC技术很容易受到重放攻击的影响,所以在这种技术中,当一辆车在CACC技术下受到攻击时,该排中的其他车辆也会受到攻击,因此采取措施减轻CACC中的此类攻击非常重要。本研究在现有无证书签名方案的基础上,采用了假名跟踪无证书聚合签名PT-CAS方案,该方案既保证了用户隐私,又利用假名跟踪算法检测和减轻攻击期间和攻击后的重放攻击。对于实验结果,我们在分析实验结果的基础上实现了算法,我们在传统的签名方案中加入了一个假名跟踪算法以保证用户的隐私,实验实现后我们发现我们提出的PT-CAS方案在计算成本、聚合签名长度和隐私保护方面都比现有的签名方案有更好的性能。在检测和减轻车辆排中众所周知的重播攻击形式方面具有97.1%的准确性。
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Pseudonym Tracking Certificateless Aggregate Signature Scheme for Preventing Replay Attacks in a Platoon of Vehicles
Cooperative Adaptive Cruise Control (CACC) is an optimistic innovation which allows connected automated vehicles to share parameters with other Connected vehicles in the advanced traffic management system in a distributed manner, thus increasing driving safety and roadway capacity and reducing energy consumption. However, CACC technology is very susceptible to replay attacks because of the connected nature of its technology, so in this technology, when a car within this platoon gets compromised the rest of the vehicles in that platoon under the CACC technology become compromised as well and so it is very important to take steps in order to mitigate such attacks in CACC. This research utilizes the pseudonym tracking certificateless aggregate signature PT-CAS scheme based on existing certificateless signature schemes which ensure user privacy as well as detect and mitigate replay attacks during and after an attack using the pseudonym tracking algorithm. For experimental results we implement algorithm after analysis of our experimental results, we incorporate a pseudonym tracking algorithm to the conventional signature schemes in order to ensure user privacy, after experimental implementation we observe that our proposed PT-CAS scheme is performatively better in terms of computational costs, length of the aggregate signature and privacy preservation than the existing signature schemes, having 97.1% accuracy in detecting and mitigating well-known forms of replay attacks in a platoon of vehicles.
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