In this paper, an innovative approach to improving the cyber-physical security of energy-efficient powertrain system in a hybrid electric vehicle (HEV) against sophisticated cyberattacks is presented. To the best of our knowledge, cyberattacks, especially sophisticated and subtle cyber-attacks, have not yet been studied in energy management systems (EMSs) for HEVs. First of all, we present a systemic assessment of long-term sophisticated cyber-attacks that aim to deteriorate the battery lifetime and energy efficiency of HEVs. Specifically, three levels of attack taxonomy according to the skill level of the attackers are considered, which are sophisticated and can hardly be detected by the human driver. In addition to levels 1 and 2 cyber-attacks that do not or partially require prior knowledge of the vehicle, we explore two other types of level 3 damage-oriented controller attacks made by highly-skilled attackers who have sufficient knowledge of the system. Such sophisticated attacks will potentially cause severe damages, such as decreasing battery capacity and energy by up to 50%. For a comprehensive vulnerability assessment, we propose innovative evaluation metrics to analyze the impact and stealthiness of sophisticated attacks. Finally, a preliminary probability-based detection method for sophisticated damage-oriented controller attacks is developed to improve the cyber-physical security of energy-efficient powertrain system in HEVs.

中文翻译：

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混合动力电动汽车节能动力系统的网络物理安全，以抵御复杂的网络攻击

在本文中，提出了一种提高混合动力电动汽车 (HEV) 中节能动力总成系统网络物理安全性的创新方法，以抵御复杂的网络攻击。据我们所知，尚未在 HEV 的能源管理系统 (EMS) 中研究网络攻击，尤其是复杂和微妙的网络攻击。首先，我们对旨在降低 HEV 电池寿命和能源效率的长期复杂网络攻击进行系统评估。具体来说，根据攻击者的技能水平考虑了三个级别的攻击分类法，这些分类法很复杂，人类驾驶员很难检测到。除了不需要或部分需要车辆先验知识的 1 级和 2 级网络攻击之外，我们探索了另外两种类型的 3 级损害导向控制器攻击，这些攻击由对系统有足够了解的高技能攻击者进行。这种复杂的攻击可能会造成严重的损害，例如将电池容量和能量降低多达 50%。对于全面的漏洞评估，我们提出了创新的评估指标来分析复杂攻击的影响和隐蔽性。最后，开发了一种针对复杂的面向损坏的控制器攻击的基于概率的初步检测方法，以提高 HEV 中节能动力总成系统的网络物理安全性。例如将电池容量和能量降低多达 50%。对于全面的漏洞评估，我们提出了创新的评估指标来分析复杂攻击的影响和隐蔽性。最后，开发了一种初步的基于概率的复杂损坏导向控制器攻击检测方法，以提高 HEV 中节能动力总成系统的网络物理安全。例如将电池容量和能量降低多达 50%。对于全面的漏洞评估，我们提出了创新的评估指标来分析复杂攻击的影响和隐蔽性。最后，开发了一种针对复杂的面向损坏的控制器攻击的基于概率的初步检测方法，以提高 HEV 中节能动力总成系统的网络物理安全性。