DCA：针对电动共享出行系统的延迟充电攻击
DCA: Delayed Charging Attack on the Electric Shared Mobility System
AI总结：
本文提出延迟充电攻击DCA，利用电动共享出行系统的物理与通信漏洞隐蔽延迟充电服务，并在纽约市真实数据上验证其可造成显著收入损失且对异常检测算法具有鲁棒性。
摘要：
电动共享出行系统（ESMS）的高效运行高度依赖于共享电动汽车（SEV）、电动汽车供电设备（EVSE）和电网之间的无缝互联。然而，这种互联性也使ESMS容易受到网络攻击，可能导致ESMS的短期故障或长期性能退化。本研究聚焦于一种具有持久影响的攻击，即延迟充电攻击（DCA），该攻击利用物理和通信漏洞隐蔽地延迟充电服务。首先，我们通过突出资产、信息流和访问点来呈现ESMS威胁模型。接着，我们识别出一系列相互关联的漏洞，将其作为发动DCA的可行攻击向量。然后，我们详细说明了DCA的实现方式，该方式能够有效绕过SEV电池管理系统中的检测以及云环境中的交叉验证。我们通过在易感-感染-移除-易感（SIRS）过程中模拟DCA动态来测试DCA模型对抗各种异常检测（AD）算法的表现，其中EVSE可能被DCA攻陷或被检测到并进行修复。利用纽约市的真实出租车出行数据和EVSE位置，DCA模型使我们能够探索长期影响并验证系统后果。结果表明，10分钟的延迟会导致排队时间延长12分钟，未满足请求增加8%，导致每位司机每周收入损失10.7%（311.7美元）。即使采用AD算法，每周收入损失仍至少为3.8%（111.8美元），且修复成本增加36,000美元，这表明DCA对AD具有鲁棒性。
英文摘要：
An efficient operation of the electric shared mobility system (ESMS) relies heavily on seamless interconnections among shared electric vehicles (SEV), electric vehicle supply equipment (EVSE), and the grid. Nevertheless, this interconnectivity also makes the ESMS vulnerable to cyberattacks that may cause short-term breakdowns or long-term degradation of the ESMS. This study focuses on one such attack with long-lasting effects, the Delayed Charge Attack (DCA), that stealthily delays the charging service by exploiting the physical and communication vulnerabilities. To begin, we present the ESMS threat model by highlighting the assets, information flow, and access points. We next identify a linked sequence of vulnerabilities as a viable attack vector for launching DCA. Then, we detail the implementation of DCA, which can effectively bypass the detection in the SEV's battery management system and the cross-verification in the cloud environment. We test the DCA model against various Anomaly Detection (AD) algorithms by simulating the DCA dynamics in a Susceptible-Infectious-Removed-Susceptible process, where the EVSE can be compromised by the DCA or detected for repair. Using real-world taxi trip data and EVSE locations in New York City, the DCA model allows us to explore the long-term impacts and validate the system consequences. The results show that a 10-min delay results in 12-min longer queuing times and 8% more unfulfilled requests, leading to a 10.7% (\$311.7) weekly revenue loss per driver. With the AD algorithms, the weekly revenue loss remains at least 3.8% (\$111.8) with increased repair costs of \$36,000, suggesting the DCA's robustness against the AD.
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