False Data Injection Attack in a Platoon of CACC: Real-Time Detection and Isolation With a PDE Approach
False Data Injection Attack in a Platoon of CACC: Real-Time Detection and Isolation With a PDE Approach
复制标题
CACC 排中的虚假数据注入攻击：使用 PDE 方法进行实时检测和隔离
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
P. Pisu
中科院分区：
文献类型：
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作者：
R. Biroon;Z. Biron;P. Pisu
互联汽车是解决交通系统中一些现有挑战的潜在解决方案，如排放、交通拥堵和燃料消耗。然而，车载通信网络存在可靠性和安全性问题。以破坏性能为目的的网络攻击可能会导致联网车辆发生灾难性碰撞，并加剧交通拥堵。为了确保安全可靠，联网车辆需要一个能够诊断网络攻击的监控系统。在这项研究中，我们考虑一排配备了协作自适应巡航控制(CACC)的互联车辆，该车辆受到一种特定类型的网络攻击，即虚假数据注入(FDI)攻击。智能FDI攻击的模型是将幽灵车辆注入联网的车辆网络，以扰乱整个系统的性能。为了便于分析，我们利用常微分方程组(ODE)建立了CACC车辆动力学的偏微分方程组模型。此外，设计了一个偏微分方程观测器来检测FDI攻击，并定位攻击在排中的注入点。利用Lyapunov稳定性理论验证了观测器在无攻击情况下的收敛，并研究了有攻击情况下残余者的行为。一个非零的恒定阈值被认为在攻击检测中提供了稳健性，尽管存在测量噪声。最后，通过仿真研究对该算法的有效性进行了评估。
Connected vehicles are potential solutions to address some of the existing challenges in transportation systems, such as emission, traffic congestion, and fuel consumption. However, vehicular communication networks endure from reliability and security issues. Cyber-attacks with performance-disrupting purposes can lead to catastrophic collisions in connected vehicles and increase traffic congestion. To ensure safety and reliability, a monitoring system with the capability of diagnosing cyber-attacks is necessary for connected vehicles. In this study, we consider a platoon of connected vehicles equipped with cooperative adaptive cruise control (CACC) that is subjected to a specific type of cyber-attack, namely “False Data Injection” (FDI) attack. A smart FDI attack is modeled with ghost vehicles injection into the connected vehicles network to disrupt the performance of the whole system. To ease the analysis, we develop a partial differential equation (PDE) model for the CACC vehicle dynamics using its ordinary differential equations (ODE). Furthermore, a PDE observer is designed to detect the FDI attack and locate the attack’s injection point in the platoon. Lyapunov stability theory has been utilized to verify the observer’s convergence under no-attack scenario and study residuals’ behaviors in the presence of the attack. A non-zero constant threshold is considered to provide robustness in attack detection despite the measurement noises. Eventually, the effectiveness of the proposed algorithm is evaluated with simulation studies.