论文标题
电动汽车充电站的机器学习支持的网络攻击预测和缓解
Machine Learning-Enabled Cyber Attack Prediction and Mitigation for EV Charging Stations
论文作者
论文摘要
在智能运输基础设施中，安全可靠的电动汽车充电站（EVCSS）已成为必要的。多年来，EVCSS的部署已迅速增加，以满足提高的充电需求。但是，信息和通信技术（ICT）的进步使这种网络物理系统（CPS）易受遭受遭受网络威胁的影响，从而破坏了充电生态系统甚至整个电网基础设施。本文开发了高级网络安全框架，该框架用于识别EVC中的潜在漏洞。此外，采用加权攻击保护树方法来创建多种攻击场景，然后开发隐藏的马尔可夫模型（HMM），并部分可观察到的蒙特卡洛计划（POMCP）算法来建模安全攻击。此外，针对已确定的威胁提出了潜在的缓解策略。
Safe and reliable electric vehicle charging stations (EVCSs) have become imperative in an intelligent transportation infrastructure. Over the years, there has been a rapid increase in the deployment of EVCSs to address the upsurging charging demands. However, advances in information and communication technologies (ICT) have rendered this cyber-physical system (CPS) vulnerable to suffering cyber threats, thereby destabilizing the charging ecosystem and even the entire electric grid infrastructure. This paper develops an advanced cybersecurity framework, where STRIDE threat modeling is used to identify potential vulnerabilities in an EVCS. Further, the weighted attack defense tree approach is employed to create multiple attack scenarios, followed by developing Hidden Markov Model (HMM) and Partially Observable Monte-Carlo Planning (POMCP) algorithms for modeling the security attacks. Also, potential mitigation strategies are suggested for the identified threats.
