Nonholonomic systems with restricted motion in specific directions usually find it challenging to stabilize to a desired equilibrium via static state feedback. Due to false data injection (FDI), the corrupted state information cannot be used for feedback, further complicating the nonholonomic systems’ stabilization problem. To address these challenges, this chapter proposes a novel secure stabilization strategy by leveraging adaptive Nussbaum-type gains, which effectively mitigate the effects of FDI attacks. The proposed strategy ensures the asymptotic stability of the closed-loop system and the boundedness of states in the presence of unknown FDI attacks. Finally, simulation results on a wheeled mobile robot validate the effectiveness of the designed control algorithm.