Factored MDP based moving target defense with dynamic threat modeling

Factored MDP based moving target defense with dynamic threat modeling

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, leveragi...

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Bibliographic Details
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
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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