论文标题
自适应多代理交通控制系统中的弹性弹性
Resilience-by-design in Adaptive Multi-Agent Traffic Control Systems
论文作者
论文摘要
连接和自动驾驶汽车（CAVS）及其不断发展的数据收集功能将在智能运输系统（ITS）支持的道路安全和效率应用中发挥重要作用，例如用于城市交通拥堵管理的交通信号控制（TSC）。但是，他们的参与将扩大安全漏洞的空间并创建更大的威胁向量。 In this paper, we perform the first detailed security analysis and implementation of a new cyber-physical attack category carried out by the network of CAVs against Adaptive Multi-Agent Traffic Signal Control (AMATSC), namely, coordinated Sybil attacks, where vehicles with forged or fake identities try to alter the data collected by the AMATSC algorithms to sabotage their decisions.因此，提出了一种新颖的游戏理论缓解方法，以最大程度地减少这种复杂的数据腐败攻击的影响。设计的Minimax游戏模型使AMATSC算法能够在可疑攻击下生成最佳决策，从而提高其弹性。在蒙特利尔市提供的交通数据集中进行了广泛的实验，以评估攻击影响。我们的结果将攻击交叉点的时间损失提高了约48.9％。可以从缓解措施中获得可观的好处，从而产生对整个网络交叉口流量的更强大的自适应控制。
Connected and Autonomous Vehicles (CAVs) with their evolving data gathering capabilities will play a significant role in road safety and efficiency applications supported by Intelligent Transport Systems (ITS), such as Traffic Signal Control (TSC) for urban traffic congestion management. However, their involvement will expand the space of security vulnerabilities and create larger threat vectors. In this paper, we perform the first detailed security analysis and implementation of a new cyber-physical attack category carried out by the network of CAVs against Adaptive Multi-Agent Traffic Signal Control (AMATSC), namely, coordinated Sybil attacks, where vehicles with forged or fake identities try to alter the data collected by the AMATSC algorithms to sabotage their decisions. Consequently, a novel, game-theoretic mitigation approach at the application layer is proposed to minimize the impact of such sophisticated data corruption attacks. The devised minimax game model enables the AMATSC algorithm to generate optimal decisions under a suspected attack, improving its resilience. Extensive experimentation is performed on a traffic dataset provided by the City of Montreal under real-world intersection settings to evaluate the attack impact. Our results improved time loss on attacked intersections by approximately 48.9%. Substantial benefits can be gained from the mitigation, yielding more robust adaptive control of traffic across networked intersections.
