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In this paper, we study the problem of assessing the effectiveness of aproactive defense-by-detection policy with a network-based moving targetdefense. We model the network system using a probabilistic attack graph--agraphical security model. Given a network system with a proactive defensestrategy, an intelligent attacker needs to perform reconnaissance repeatedly tolearn about the locations of intrusion detection systems and re-plan optimallyto reach the target while avoiding detection. To compute the attacker'sstrategy for security evaluation, we develop a receding-horizon planningalgorithm using a risk-sensitive Markov decision process with a time-varyingreward function. Finally, we implement both defense and attack strategies in asynthetic network and analyze how the frequency of network randomization andthe number of detection systems can influence the success rate of the attacker.This study provides insights for designing proactive defense strategies againstonline and multi-stage attacks by a resourceful attacker.

A Receding-Horizon MDP Approach for Performance Evaluation of Moving Target Defense in Networks