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• W2765449849 abstract "Emerging zero-day vulnerabilities in information and communications technology systems make cyber defenses very challenging. In particular, the defender faces uncertainties of; e.g., system states and the locations and the impacts of vulnerabilities. In this paper, we study the defense problem on a computer network that is modeled as a partially observable Markov decision process on a Bayesian attack graph. We propose online algorithms which allow the defender to identify effective defense policies when utility functions are unknown a priori. The algorithm performance is verified via numerical simulations based on real-world attacks." @default.
• W2765449849 created "2017-11-10" @default.
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• W2765449849 date "2017-10-30" @default.
• W2765449849 modified "2023-09-30" @default.
• W2765449849 title "Online Algorithms for Adaptive Cyber Defense on Bayesian Attack Graphs" @default.
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• W2765449849 doi "https://doi.org/10.1145/3140549.3140556" @default.
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