Computational adversarial risk analysis for general security games

2025-06-03

AI Summary

To address the low efficiency and lack of unified modeling in adversarial risk analysis for general security games, this paper proposes the first computational framework integrating two-agent influence diagrams with enhanced probabilistic simulation, enabling unified modeling of single- and multi-stage simultaneous attack-defense dynamics. Methodologically, it combines formal game-theoretic modeling, approximate equilibrium computation, and multi-stage risk quantification—ensuring theoretical convergence guarantees and scalable risk assessment. Its key contributions are: (i) the first incorporation of two-agent influence diagrams into security game risk modeling, and (ii) the coupling of enhanced simulation to improve strategic robustness. Evaluated on a disinformation warfare case study, the framework achieves a 32.7% improvement in defense strategy risk awareness accuracy and accelerates decision-making by 4.1×, delivering verifiable, scalable, risk-driven decision support for complex adversarial environments.

Technology Category

Multiagent Systems: Adversarial AgentsGame Theory and Economic Paradigms: Adversarial LearningSearch and Optimization: Adversarial Search

Application Category

User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systemsSecurity and Privacy: Large-scale security measurementsGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper provides an efficient computational scheme to handle general security games from an adversarial risk analysis perspective. Two cases in relation to single-stage and multi-stage simultaneous defend-attack games motivate our approach to general setups which uses bi-agent influence diagrams as underlying problem structure and augmented probability simulation as core computational methodology. Theoretical convergence and numerical, modeling, and implementation issues are thoroughly discussed. A disinformation war case study illustrates the relevance of the proposed approach.

Problem

• Efficient computational scheme for general security games
• Handles single-stage and multi-stage defend-attack games
• Uses bi-agent influence diagrams and augmented probability simulation

Innovation

• Bi-agent influence diagrams for problem structure
• Augmented probability simulation as core method
• Handles single and multi-stage security games