Adaptive Moving Target Defense in Web Applications and Networks using Factored MDP

Megha Bose, Praveen Paruchuri, Akshat Kumar

Abstract: Moving Target Defense (MTD) has emerged as a proactive and dynamic framework to counteract evolving cyber threats. Traditional MTD approaches often rely on assumptions about the attacker’s knowledge and behavior. However, real-world scenarios are inherently more complex, with adaptive attackers and limited prior knowledge of their payoffs and intentions. This paper1 introduces a novel approach to MTD using a Markov Decision Process (MDP) model that does not rely on predefined attacker payoffs. Our framework integrates the attacker’s real-time responses into the defender’s MDP using a dynamic Bayesian Network. By employing a factored MDP model, we provide a comprehensive and realistic representation of the system and its dependencies. We also incorporate incremental updates to an attack response predictor as new data emerges. This ensures an adaptive and robust defense mechanism. Additionally, we consider the costs of switching configurations in MTD, integrating them into the reward structure to balance execution and defense costs. We first highlight the challenges of the problem through a theoretical negative result on regret. However, our empirical evaluations, conducted in network and web application settings, demonstrate the framework’s effectiveness in environments characterized by high uncertainty and dynamically changing attack surfaces.