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NSGZero: Efficiently Learning Non-Exploitable Policy in Large-Scale Network Security Games with Neural Monte Carlo Tree Search. (arXiv:2201.07224v1 [cs.CR])
Jan. 20, 2022, 2:10 a.m. | Wanqi Xue, Bo An, Chai Kiat Yeo
cs.LG updates on arXiv.org arxiv.org
How resources are deployed to secure critical targets in networks can be
modelled by Network Security Games (NSGs). While recent advances in deep
learning (DL) provide a powerful approach to dealing with large-scale NSGs, DL
methods such as NSG-NFSP suffer from the problem of data inefficiency.
Furthermore, due to centralized control, they cannot scale to scenarios with a
large number of resources. In this paper, we propose a novel DL-based method,
NSGZero, to learn a non-exploitable policy in NSGs. NSGZero …
More from arxiv.org / cs.LG updates on arXiv.org
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