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\textit{Intelligent Navigation Systems} (INS) are exposed to an increasingnumber of informational attack vectors, which often intercept through thecommunication channels between the INS and the transportation network duringthe data collecting process. To measure the resilience of INS, we use theconcept of a Wardrop Non-Equilibrium Solution (WANES), which is characterizedby the probabilistic outcome of learning within a bounded number ofinteractions. By using concentration arguments, we have discovered that anybounded feedback delaying attack only degrades the systematic performance up toorder $\tilde{\mathcal{O}}(\sqrt{{d^3}{T^{-1}}})$ along the traffic flowtrajectory 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 WardropNon-equilibrium even when experiencing a certain period of disruption in theinformation structure. These findings provide valuable insights for designingdefense mechanisms against possible jamming attacks across different layers ofthe transportation ecosystem.

Is Stochastic Mirror Descent Vulnerable to Adversarial Delay Attacks? A Traffic Assignment Resilience Study