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Stackelberg Security Games with Contagious Attacks on a Network: Reallocation to the Rescue

  • Rufan Bai
  • , Haoxing Lin
  • , Xinyu Yang
  • , Xiaowei Wu
  • , Minming Li
  • , Weijia Jia

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Abstract

In the classic network security games, the defender distributes defending resources to the nodes of the network, and the attacker attacks a node, with the objective of maximizing the damage caused. In this paper, we consider the network defending problem against contagious attacks, e.g., the attack at a node u spreads to the neighbors of u and can cause damage at multiple nodes. Existing works that study shared resources assume that the resource allocated to a node can be shared or duplicated between neighboring nodes. However, in the real world, sharing resource naturally leads to a decrease in defending power of the source node, especially when defending against contagious attacks. Therefore, we study the model in which resources allocated to a node can only be transferred to its neighboring nodes, which we refer to as a reallocation process. We show that the problem of computing optimal defending strategy is NP-hard even for some very special cases. For positive results, we give a mixed integer linear program formulation for the problem and a bi-criteria approximation algorithm. Our experimental results demonstrate that the allocation and reallocation strategies our algorithm computes perform well in terms of minimizing the damage due to contagious attacks. © 2023 The Authors.
Original languageEnglish
Pages (from-to)487-515
JournalJournal of Artificial Intelligence Research
Volume77
Online published19 Jun 2023
DOIs
Publication statusPublished - 2023

Funding

This work was supported by the Science and Technology Development Fund (FDCT) Macau SAR (file no. 0014/2022/AFJ, 0085/2022/A, 0143/2020/A3, SKL-IOTSC-2021-2023). Experiments were conducted at SICC supported by SKL-IOTSC, University of Macau. Minming Li was supported by the Fundamental Research Funds for the Central Universities. Weijia Jia's work was supported in part by the Guangdong Key Lab of AI and Multi-modal Data Processing, United International College (UIC), Zhuhai under Grant 2020KSYS007 sponsored by Guangdong Provincial Department of Education; in part by the Chinese National Research Fund (NSFC) under Grant 62272050; in part by Institute of Artificial Intelligence and Future Networks (BNU-Zhuhai) and Engineering Center of AI and Future Education, Guangdong Provincial Department of Science and Technology, China; Zhuhai Science-Tech Innovation Bureau under Grants ZH22017001210119PWC and 28712217900001, and in part by the Interdisciplinary Intelligence SuperComputer Center of Beijing Normal University (Zhuhai).

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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