Toward Robust Monitoring of Malicious Outbreaks

Shaojie Tang, Siyuan Liu, Xu Han, Yu Qiao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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 ofmonitors in the network that canminimize attacker's success ratio in the worst-case. (We also extend the basic model by considering a noisy diffusionmodel, 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.

Original languageEnglish (US)
Pages (from-to)1257-1271
Number of pages15
JournalINFORMS Journal on Computing
Volume34
Issue number2
DOIs
StatePublished - Mar 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research

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