Unveiling the Shadows—A Framework for APT’s Defense AI and Game Theory Strategy

Advanced persistent threats (APTs) pose significant risks to critical systems and infrastructures due to their stealth and persistence. While several studies have reviewed APT characteristics and defense mechanisms, this paper goes further by proposing a hybrid defense framework based on artificial intelligence and game theory. First, a literature review outlines the evolution, methodologies, and known incidents of APTs. Then, a novel conceptual framework is presented, integrating unsupervised anomaly detection (isolation forest) and strategic defense modeling (Stackelberg game). Experimental results on simulated data demonstrate the robustness and scalability of the approach. In addition to reviewing current APT detection techniques, this work presents a defense model that integrates machine learning-based anomaly detection with predictive game-theoretic modeling.