论文标题
使用贝叶斯网络评估连接和自动驾驶汽车的系统级网络攻击漏洞
Assessment of System-Level Cyber Attack Vulnerability for Connected and Autonomous Vehicles Using Bayesian Networks
论文作者
论文摘要
这项研究提出了一种量化网络攻击脆弱性及其影响的方法，这些方法是基于概率图形模型在连接和自动驾驶汽车框架下针对智能运输系统的概率图形模型。基于所选性能指标的智能信号和合作自适应巡航控制（CACC）应用程序，计算出各种类型的网络攻击漏洞及其影响。给出了数值示例，这些示例显示了漏洞在平均相交队列长度，停止次数，平均速度和延迟方面的影响。在具有和没有冗余系统的信号网络中，漏洞可以将平均队列和延误增加$ 3 \％$和$ 15 \％$和$ 4 \％$和$ 17 \％$。对于CACC应用程序，当低速信息扰动时，影响水平平均达到$ 50 \％$延迟差。当攻击者插入显着不同的速度特征时，延迟差的增加超过了正常交通状况的$ 100 \％$。
This study presents a methodology to quantify vulnerability of cyber attacks and their impacts based on probabilistic graphical models for intelligent transportation systems under connected and autonomous vehicles framework. Cyber attack vulnerabilities from various types and their impacts are calculated for intelligent signals and cooperative adaptive cruise control (CACC) applications based on the selected performance measures. Numerical examples are given that show impact of vulnerabilities in terms of average intersection queue lengths, number of stops, average speed, and delays. At a signalized network with and without redundant systems, vulnerability can increase average queues and delays by $3\%$ and $15\%$ and $4\%$ and $17\%$, respectively. For CACC application, impact levels reach to $50\%$ delay difference on average when low amount of speed information is perturbed. When significantly different speed characteristics are inserted by an attacker, delay difference increases beyond $100\%$ of normal traffic conditions.
