Evolutionary, Mean-Field and Pressure-Resistance Game Modelling of Networks Security

TL;DR

This paper introduces a comprehensive framework combining mean-field, evolutionary, and pressure-resistance game models to analyze large-scale network security and crime prevention, focusing on complex player interactions.

Contribution

It develops an extended model integrating different game approaches for large networks, and analyzes solutions under specific asymptotic regimes.

Findings

Constructed a class of solutions with the turnpike property.

Integrated multiple game models for complex network interactions.

Provided asymptotic analysis for practical computation.

Abstract

The recently developed mean-field game models of corruption and bot-net defence in cyber-security, the evolutionary game approach to inspection and corruption, and the pressure-resistance game element, can be combined under an extended model of interaction of large number of indistinguishable small players against a major player, with focus on the study of security and crime prevention. In this paper we introduce such a general framework for complex interaction in network structures of many players, that incorporates individual decision making inside the environment (the mean-field game component), binary interaction (the evolutionary game component), and the interference of a principal player (the pressure-resistance game component). To perform concrete calculations with this overall complicated model we work in three basic asymptotic regimes; fast execution of personal decisions, small rates of binary interactions, and small payoff discounting in time. By this approach we construct a class of solutions having the so-called turnpike property.