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Toward Robust Monitoring of Malicious Outbreaks

Author

Listed:
  • Shaojie Tang

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080)

  • Siyuan Liu

    (Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802)

  • Xu Han

    (Gabelli School of Business, Fordham University, New York, New York 10023)

  • Yu Qiao

    (Department of Computer Science, Nanjing University, Nanjing, Jiangsu Province 210093, China)

Abstract

Recently, diffusion processes in social networks have attracted increasing attention within computer science, marketing science, social sciences, and political science. Although the majority of existing works focus on maximizing the reach of desirable diffusion processes, we are interested in deploying a group of monitors to detect malicious diffusion processes such as the spread of computer worms. In this work, we introduce and study the ( α , β ) -Monitoring Game} on networks. Our game is composed of two parties an attacker and a defender. The attacker can launch an attack by distributing a limited number of seeds (i.e., virus) to the network. Under our ( α , β ) -Monitoring Game, we say an attack is successful if and only if the following two conditions are satisfied: (1) the outbreak/propagation reaches at least α individuals without intervention, and (2) it has not been detected before reaching β individuals. Typically, we require that β is no larger than α in order to compensate the reaction delays after the outbreak has been detected. On the other end, the defender’s ultimate goal is to deploy a set of monitors in the network that can minimize attacker’s success ratio in the worst-case. (We also extend the basic model by considering a noisy diffusion model, where the propagation probabilities on each edge could vary within an interval.) Our work is built upon recent work in security games, our adversarial setting provides robust solutions in practice. Summary of Contribution: Although the diffusion processes in social networks have been extensively studied, most existing works aim at maximizing the reach of desirable diffusion processes. We are interested in deploying a group of monitors to detect malicious diffusion processes, such as the spread of computer worms. To capture the impact of model uncertainty, we consider a noisy diffusion model in which the propagation probabilities on each edge could vary within an interval. Our work is built upon recent work in security games; our adversarial setting leads to robust solutions in practice.

Suggested Citation

  • Shaojie Tang & Siyuan Liu & Xu Han & Yu Qiao, 2022. "Toward Robust Monitoring of Malicious Outbreaks," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 1257-1271, March.
  • Handle: RePEc:inm:orijoc:v:34:y:2022:i:2:p:1257-1271
    DOI: 10.1287/ijoc.2021.1077
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    References listed on IDEAS

    as
    1. Alan Washburn & Kevin Wood, 1995. "Two-Person Zero-Sum Games for Network Interdiction," Operations Research, INFORMS, vol. 43(2), pages 243-251, April.
    2. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions," LIDAM Reprints CORE 341, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions - 1," LIDAM Reprints CORE 334, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    Full references (including those not matched with items on IDEAS)

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