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Moving Target Defense (MTD) is an emerging game-changing defense strategy incybersecurity with the goal of strengthening defenders and conversely puzzlingadversaries in a network environment. The successful deployment of an MTDsystem can be affected by several factors including 1) the effectiveness of theemployed technique, 2) the deployment strategy, 3) the cost of the MTDimplementation, and 4) the impact yielded by the enforced security policies.Many research efforts have been spent on introducing a variety of MTDtechniques which are often evaluated through simulations. Nevertheless, thisline of research needs more attention. In particular, the determination ofoptimal cost and policy analysis and the selection of those policies in an MTDsetting is still an open research question.  To advance the state-of-the-art of this line of research, this paperintroduces an approach based on control theory to model, analyze and selectoptimal security policies for Moving Target Defense (MTD) deploymentstrategies. A Markov Decision Process (MDP) scheme is presented to model statesof the system from attacking point of view. The employed value iteration methodis based on the Bellman optimality equation for optimal policy selection foreach state defined in the system. The model is then utilized to analyze theimpact of various costs on the optimal policy. The MDP model is then applied totwo case studies to evaluate the performance of the model.

Markov Decision Process to Enforce Moving Target Defence Policies