NIGA: A Novel Method for Investigating the Attacker–Defender Model within Critical Infrastructure Networks

Jiaqi Ren, Jin Liu, Yibo Dong, Zhe Li and Weili Li ()
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Jiaqi Ren: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Jin Liu: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Yibo Dong: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Zhe Li: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China
Weili Li: National Key Laboratory of Information Systems Engineering, National University of Defense Technology, Changsha 410073, China

Mathematics, 2024, vol. 12, issue 16, 1-24

Abstract: The field of infrastructure security has garnered significant research attention. By integrating complex network theory with game theory, researchers have proposed many methods for studying the interactions between the attacker and the defender from a macroscopic viewpoint. We constructed a game model of infrastructure networks to analyze attacker-defender confrontations. To address the challenge of finding the Nash equilibrium, we developed a novel algorithm—node-incremental greedy algorithm (NIGA)—which uses less strategy space to solve the problem. The experiments performed further showed that NIGA has better optimization ability than other traditional algorithms. The optimal defense strategies under different conditions of initial strategy ratios and attacker-defender resources were analyzed in this study. Using intelligent computing to solve the Nash equilibrium is a new approach by which for researchers to analyze attacker-defender confrontations.

Keywords: infrastructure attack and defense; complex networks; game theory; intelligent computing; Nash equilibrium (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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