Covert Vehicle Misguidance and Its Detection: A Hypothesis Testing Game over Continuous-Time Dynamics

Abstract

We formulate a stochastic zero-sum game over continuous-time dynamics to analyze the competition between the attacker, who tries to covertly misguide the vehicle to an unsafe region, versus the detector, who tries to detect the attack signal based on the observed trajectory of the vehicle. Based on Girsanov's theorem and the generalized Neyman-Pearson lemma, we show that a constant bias injection attack as the attacker's strategy and a likelihood ratio test as the detector's strategy constitute the unique saddle point of the game. We also derive the first-order and the second-order exponents of the type II error as a function of the data length.