  The security in networked systems depends greatly on recognizing and
identifying adversarial behaviors. Traditional detection methods focus on
specific categories of attacks and have become inadequate for increasingly
stealthy and deceptive attacks that are designed to bypass detection
strategically. This work aims to develop a holistic theory to countermeasure
such evasive attacks. We focus on extending a fundamental class of
statistical-based detection methods based on Neyman-Pearson's (NP) hypothesis
testing formulation. We propose game-theoretic frameworks to capture the
conflicting relationship between a strategic evasive attacker and an
evasion-aware NP detector. By analyzing both the equilibrium behaviors of the
attacker and the NP detector, we characterize their performance using
Equilibrium Receiver-Operational-Characteristic (EROC) curves. We show that the
evasion-aware NP detectors outperform the passive ones in the way that the
former can act strategically against the attacker's behavior and adaptively
modify their decision rules based on the received messages. In addition, we
extend our framework to a sequential setting where the user sends out
identically distributed messages. We corroborate the analytical results with a
case study of anomaly detection.
