NSGZero: Efficiently Learning Non-Exploitable Policy in Large-Scale Network Security Games with Neural Monte Carlo Tree Search. (arXiv:2201.07224v1 [cs.CR])

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 …

games large network network security non policy search security