Application of Stochastic Optimal Control, Game Theory and Information Fusion for Cyber Defense Modelling

The present paper addresses an effective cyber defense model by applying information fusion based game theoretical approaches. In the present paper, we are trying to improve previous models by applying stochastic optimal control and robust optimization techniques. Jump processes are applied to model different and complex situations in cyber games. Applying jump processes we propose some models for cyber battle spaces. The resulted stochastic models are solved by applying stochastic optimal control methods. A robust optimization technique is proposed to obtain robust estimations in the case of lack of complete data. We address reinforcement learning throughout the by stochastic optimal control formulation. Previous models are improved by applying optimal control approaches to overcome the issue of time steps in game theory based approaches in which times steps cause limitations by considering the cases that may take longer times. Two adaptation methods are proposed in incomplete information cases.