Advances in Attack–Defense Game Models for IIoT: A Review

The industrial internet of things (IIoT) significantly increases industrial productivity but also brings more network security threats.The evolving diversity of cyber attacks has exacerbated security risks in IIoT systems, rendering conventional passive defense mechanisms inadequate against sophisticated intrusions, such as advanced persistent threat(APT), distributed denial of services(DDoS) etc.. This necessitates the adoption of proactive defense strategies, where game theory emerges as a powerful mathematical framework for modeling dynamic attack-defense interactions. Unlike existing surveys that focus solely on game theory fundamentals or IIoT security mechanisms, this paper establishes a novel three-dimensional methodological framework for systematically reviewing game-theoretic applications in IIoT security: (1) theoretical foundations (game components and taxonomy), (2) model implementations (eight principal game types), and (3) comparative analyses (advantages/limitations per model). Crucially, we introduce an innovative classification perspective based on player relationships and payoff computation methods - a significant departure from prior categorizations. We first establish the theoretical framework by examining essential game components and taxonomy classifications. Subsequently, we investigate current research progress through the lens of these eight principal game models, conducting a comprehensive comparative analysis of their category,advantages,drawbacks. The study further identifies three fundamental limitations in existing game-theoretic approaches: imperfect information processing, dynamic adaptation constraints, and multi-agent coordination challenges. Our critical analysis proposed future research directions emphasizing hybrid defense mechanisms, machine learning-enhanced game models, and real-time response architectures. This structured review not only fills the gap in IIoT-specific game-theoretic security surveys but also provides researchers with a unified conceptual framework for developing adaptive security solutions.