A Markov Decision Process to Determine Optimal Policies in Moving Target

Moving Target Defense (MTD) has been introduced as a new game changer strategy in cybersecurity to strengthen defenders and conversely weaken adversaries. The successful implementation of an MTD system can be influenced by several factors including the effectiveness of the employed technique, the deployment strategy, the cost of the MTD implementation, and the impact from the enforced security policies. Several efforts have been spent on introducing various forms of MTD techniques. However, insufficient research work has been conducted on cost and policy analysis and more importantly the selection of these policies in an MTD-based setting. This poster paper proposes a Markov Decision Process (MDP) modeling-based approach to analyze security policies and further select optimal policies for moving target defense implementation and deployment. The adapted value iteration method would solve the Bellman Optimality Equation for optimal policy selection for each state of the system. The results of some simulations indicate that such modeling can be used to analyze the impact of costs of possible actions towards the optimal policies.