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- W4323572012 abstract "The increasing connectivity and intricate remote access environment have made traditional perimeter-based network defense vulnerable. Zero trust becomes a promising approach to provide defense policies based on agent-centric trust evaluation. However, the limited observations of the agent's trace bring information asymmetry in the decision-making. To facilitate the human understanding of the policy and the technology adoption, one needs to create a zero-trust defense that is explainable to humans and adaptable to different attack scenarios. To this end, we propose a scenario-agnostic zero-trust defense based on Partially Observable Markov Decision Processes (POMDP) and first-order Meta-Learning using only a handful of sample scenarios. The framework leads to an explainable and generalizable trust-threshold defense policy. To address the distribution shift between empirical security datasets and reality, we extend the model to a robust zero-trust defense minimizing the worst-case loss. We use case studies and real-world attacks to corroborate the results." @default.
- W4323572012 created "2023-03-09" @default.
- W4323572012 creator A5076076111 @default.
- W4323572012 creator A5081500464 @default.
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- W4323572012 date "2023-03-06" @default.
- W4323572012 modified "2023-10-16" @default.
- W4323572012 title "Scenario-Agnostic Zero-Trust Defense with Explainable Threshold Policy: A Meta-Learning Approach" @default.
- W4323572012 doi "https://doi.org/10.48550/arxiv.2303.03349" @default.
- W4323572012 hasPublicationYear "2023" @default.
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