  \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.
