随机镜像下降是否容易受到对抗性延迟攻击？一项交通分配弹性研究
Is Stochastic Mirror Descent Vulnerable to Adversarial Delay Attacks? A Traffic Assignment Resilience Study
AI总结：
本研究探讨了智能导航系统在对抗性延迟攻击下的弹性，发现延迟镜像下降框架在有限交互内性能下降有限，仍能实现Wardrop非均衡，为防御机制设计提供了见解。
摘要：
智能导航系统（INS）正面临日益增多的信息攻击向量，这些攻击通常在数据收集过程中通过INS与交通网络之间的通信信道进行拦截。为了衡量INS的弹性，我们使用了Wardrop非均衡解（WANES）的概念，其特征是在有限次数交互内学习的概率结果。通过使用集中不等式论证，我们发现，在延迟镜像下降（DMD）在线学习框架内，任何有界的反馈延迟攻击仅使系统性能沿交通流轨迹下降至$\tilde{\mathcal{O}}(\sqrt{{d^3}{T^{-1}}})$阶。这种性能下降仅需施加温和的假设即可发生。我们的结果表明，基于学习的INS基础设施即使在信息结构中经历一定时期的破坏，也能实现Wardrop非均衡。这些发现为设计针对交通生态系统不同层面可能出现的干扰攻击的防御机制提供了有价值的见解。
英文摘要：
\textit{Intelligent Navigation Systems} (INS) are exposed to an increasing number of informational attack vectors, which often intercept through the communication channels between the INS and the transportation network during the data collecting process. To measure the resilience of INS, we use the concept of a Wardrop Non-Equilibrium Solution (WANES), which is characterized by the probabilistic outcome of learning within a bounded number of interactions. By using concentration arguments, we have discovered that any bounded feedback delaying attack only degrades the systematic performance up to order $\tilde{\mathcal{O}}(\sqrt{{d^3}{T^{-1}}})$ along the traffic flow trajectory within the Delayed Mirror Descent (DMD) online-learning framework. This degradation in performance can occur with only mild assumptions imposed. Our result implies that learning-based INS infrastructures can achieve Wardrop Non-equilibrium even when experiencing a certain period of disruption in the information structure. These findings provide valuable insights for designing defense mechanisms against possible jamming attacks across different layers of the transportation ecosystem.
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