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.

翻译：网络系统的安全性在很大程度上依赖于对敌对行为的识别与察觉。传统检测方法专注于特定攻击类别，已难以应对为策略性绕过检测而设计的日益隐蔽和具有欺骗性的攻击。本研究旨在建立一套整体性理论来对抗此类规避攻击。我们重点拓展基于奈曼-皮尔逊（NP）假设检验框架的一类基础性统计检测方法。通过构建博弈论框架，我们捕捉策略性规避攻击者与规避感知NP检测器之间的冲突关系。通过分析攻击者与NP检测器的均衡行为，我们利用均衡接收者操作特性（EROC）曲线刻画其性能。研究表明，规避感知NP检测器优于被动检测器之处在于：前者能针对攻击者行为采取策略性行动，并根据接收到的信息自适应调整决策规则。此外，我们将该框架扩展至用户发送独立同分布消息的序贯场景，并通过异常检测案例验证了理论分析结果。