  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 improves data
efficiency by performing planning with neural Monte Carlo Tree Search (MCTS).
Our main contributions are threefold. First, we design deep neural networks
(DNNs) to perform neural MCTS in NSGs. Second, we enable neural MCTS with
decentralized control, making NSGZero applicable to NSGs with many resources.
Third, we provide an efficient learning paradigm, to achieve joint training of
the DNNs in NSGZero. Compared to state-of-the-art algorithms, our method
achieves significantly better data efficiency and scalability.
