Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2024
Abstract
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.
Keywords
Moving target defense, Markov decision process, Adaptive strategy, Uncertainty
Discipline
Information Security
Publication
Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024) : Auckland, New Zealand, May 6-10
First Page
2165
Last Page
2167
Publisher
International Foundation for Autonomous Agents and Multiagent Systems
City or Country
Richland, SC
Citation
BOSE, Megha; PARUCHURI, Praveen; and KUMAR, Akshat.
Factored MDP based moving target defense with dynamic threat modeling. (2024). Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024) : Auckland, New Zealand, May 6-10. 2165-2167.
Available at: https://ink.library.smu.edu.sg/sis_research/9907
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
