Mean-field-game model for Botnet defense in Cyber-security

Abstract

We initiate the analysis of the response of computer owners to various offers of defence systems against a cyber-hacker (for instance, a botnet attack), as a stochastic game of a large number of interacting agents. We introduce a simple mean-field game that models their behavior. It takes into account both the random process of the propagation of the infection (controlled by the botner herder) and the decision making process of customers. Its stationary version turns out to be exactly solvable (but not at all trivial) under an additional natural assumption that the execution time of the decisions of the customers (say, switch on or out the defence system) is much faster that the infection rates.

References in corpus (2)

• Mean field game model of corruption
• The evolutionary game of pressure (or interference), resistance and collaboration

Cited by in corpus (7)

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• Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning
• The evolutionary game of pressure (or interference), resistance and collaboration
• Stationary Equilibria of Mean Field Games with Finite State and Action Space
• Household epidemic models and McKean-Vlasov Poisson driven stochastic differential equations
• A Probabilistic Approach to Extended Finite State Mean Field Games
• Evolutionary, Mean-Field and Pressure-Resistance Game Modelling of Networks Security