The high-level integration and interaction between the information flow at the cyber layer and the physical subjects at the vehicular layer enables the connected automated vehicles (CAVs) to achieve rapid, cooperative and shared travel. However, the cyber layer is challenged by malicious attacks and the shortage of communication resources, which makes the vehicular layer suffer from system nonlinearity, disturbance randomness and behavior uncertainty, thus interfering with the stable operation of the platoon. So far, scholars usually adopt the method of assuming or improving the car-following model to explore the platoon behavior and the defense mechanism in cyberattacks, but they have not considered whether the model itself has disturbance and impact on cyberattack defenses. In other words, it is still being determined whether the car-following model designed can be fully applicable to such cyberattacks. To provide a theoretical basis for vehicular layer modeling, it is necessary to comprehend the self-resistance of different car-following models faced on various cyberattacks. First, we review the car-following models adopted on the vehicular layer in cyberattacks, involving traffic engineering, physical statistics, and platoon dynamics. Based on the review, we divide the malicious attacks faced by the cyber layer into explicit attacks and implicit attacks. Second, we develop a cooperative generalized force model (CGFM), which combines and unifies the r-predecessors following communication topology. The proposed models, labeled the vulnerable cooperative intelligent driver model (VCIDM), the vulnerable cooperative optimal velocity model (VCOVM), and the vulnerable cooperative platoon dynamics model (VCPDM), incorporate the CGFM model and assorted cyberattack injection modes to explain the cyberattack effects on the platoon self-resistance capability. Upon the described models, we provide six indicators in three dimensions from the basic traffic element, including drivers, vehicles, and environment. These indicators illustrate driver tolerance, vehicle adaptability, and environmental resistance when a platoon faces attacks such as bogus information, replay/delay, and communication interruption. We arrange and reorganize the car-following models and the cyberattack injection modes to complete the research on the self-resistance capability of the platoon, which has positive research value and practical significance for enhancing the endogenous security at the vehicular layer and improving the intrusion tolerability at the cyber layer.

中文翻译：

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不同网络攻击注入模式下互联自动车辆排的自我抵抗力建模与分析

网络层的信息流与车辆层的物理主体之间的高度集成和交互，使互联自动车辆（CAV）能够实现快速、协作和共享的出行。然而，网络层受到恶意攻击和通信资源短缺的挑战，使得车辆层面临系统非线性、扰动随机性和行为不确定性，从而干扰排的稳定运行。迄今为止，学者们通常采用假设或改进跟驰模型的方法来探讨网络攻击中的编队行为和防御机制，但没有考虑模型本身是否对网络攻击防御产生干扰和影响。也就是说，所设计的跟车模型是否能够完全适用于此类网络攻击，目前仍在确定。为了给车辆层建模提供理论基础，有必要了解不同跟驰模型对各种网络攻击的自我抵抗能力。首先，我们回顾了网络攻击中车辆层采用的跟车模型，涉及交通工程、物理统计和队列动力学。在此基础上，我们将网络层面临的恶意攻击分为显式攻击和隐式攻击。其次，我们开发了一种协作广义力模型（CGFM），它结合并统一了遵循通信拓扑的 r 前驱模型。所提出的模型被称为易受攻击的协作智能驾驶员模型（VCIDM）、易受攻击的协作最佳速度模型（VCOVM）和易受攻击的协作排动力学模型（VCPDM），结合了 CGFM 模型和各种网络攻击注入模式来解释网络攻击的影响关于排的自我抵抗能力。在所描述的模型上，我们从驾驶员、车辆和环境三个基本交通要素出发，提供了三个维度的六个指标。这些指标说明了当车队面临虚假信息、重放/延迟和通信中断等攻击时，驾驶员的容忍度、车辆适应性和环境抵抗力。我们对跟驰模型和网络攻击注入模式进行整理和重组，完成了车队自我抵抗能力的研究，对于增强车辆层内生安全、提高入侵容忍能力具有积极的研究价值和现实意义。在网络层。