Proceedings

Solving Large-Scale Extensive-Form Network Security Games via Neural Fictitious Self-Play

Wanqi Xue, Youzhi Zhang, Shuxin Li, Xinrun Wang, Bo An, Chai Kiat Yeo

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3713-3720.

https://doi.org/10.24963/ijcai.2021/511

Keywords

  • Multidisciplinary Topics and Applications: Computational Sustainability
  • Multidisciplinary Topics and Applications: Security and Privacy

Abstract

Securing networked infrastructures is important in the real world. The problem of deploying security resources to protect against an attacker in networked domains can be modeled as Network Security Games (NSGs). Unfortunately, existing approaches, including the deep learning-based approaches, are inefficient to solve large-scale extensive-form NSGs. In this paper, we propose a novel learning paradigm, NSG-NFSP, to solve large-scale extensive-form NSGs based on Neural Fictitious Self-Play (NFSP). Our main contributions include: i) reforming the best response (BR) policy network in NFSP to be a mapping from action-state pair to action-value, to make the calculation of BR possible in NSGs; ii) converting the average policy network of an NFSP agent into a metric-based classifier, helping the agent to assign distributions only on legal actions rather than all actions; iii) enabling NFSP with high-level actions, which can benefit training efficiency and stability in NSGs; and iv) leveraging information contained in graphs of NSGs by learning efficient graph node embeddings. Our algorithm significantly outperforms state-of-the-art algorithms in both scalability and solution quality.

Wanqi Xue, Youzhi Zhang, Shuxin Li, Xinrun Wang, Bo An, and Chai Kiat Yeo. 2021. Solving Large-Scale Extensive-Form Network Security Games via Neural Fictitious Self-Play. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3713–3720. https://doi.org/10.24963/ijcai.2021/511
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