Factored MDP based moving target defense with dynamic threat modeling

• Main Authors: BOSE, Megha, PARUCHURI, Praveen, KUMAR, Akshat
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Moving target defense; Markov decision process; Adaptive strategy; Uncertainty; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/9907; https://ink.library.smu.edu.sg/context/sis_research/article/10907/viewcontent/p2165.pdf
• Institution: Singapore Management University

Moving Target Defense (MTD) has emerged as a proactive defense framework to counteract ever-changing cyber threats. Existing approaches often make assumptions about attacker-side knowledge and behavior, potentially resulting in suboptimal defense. This paper introduces a novel MTD approach, leveraging a Markov Decision Process (MDP) model that eliminates the need for prior knowledge about attacker intentions or payoffs. Our framework seamlessly integrates real-time attacker responses into the defender's MDP using a dynamic Bayesian network. We use a factored MDP model to enable a more comprehensive and realistic representation of the system having multiple switchable aspects and also accommodate incremental updates of an attack response predictor as new attack data emerges, ensuring adaptive defense. Empirical evaluations demonstrate the approach's effectiveness in uncertain scenarios with evolving as well as unknown attack landscapes.