Infection Spreading and Source Identification: A Hide and Seek Game

Abstract

The goal of an infection source node (e.g., a rumor or computer virus source) in a network is to spread its infection to as many nodes as possible, while remaining hidden from the network administrator. On the other hand, the network administrator aims to identify the source node based on knowledge of which nodes have been infected. We model the infection spreading and source identification problem as a strategic game, where the infection source and the network administrator are the two players. As the Jordan center estimator is a minimax source estimator that has been shown to be robust in recent works, we assume that the network administrator utilizes a source estimation strategy that can probe any nodes within a given radius of the Jordan center. Given any estimation strategy, we design a best-response infection strategy for the source. Given any infection strategy, we design a best-response estimation strategy for the network administrator. We derive conditions under which a Nash equilibrium of the strategic game exists. Simulations in both synthetic and real-world networks demonstrate that our proposed infection strategy infects more nodes while maintaining the same safety margin between the true source node and the Jordan center source estimator.

References in corpus (5)

• Evolutionary Dynamics of Information Diffusion over Social Networks
• Robust Decentralized Detection and Social Learning in Tandem Networks
• Whose Opinion to follow in Multihypothesis Social Learning? A Large Deviations Perspective
• On the Universality of Jordan Centers for Estimating Infection Sources in Tree Networks
• Spy vs. Spy: Rumor Source Obfuscation

Cited by in corpus (8)

• Contagion Source Detection in Epidemic and Infodemic Outbreaks: Mathematical Analysis and Network Algorithms
• Quickest Change Detection in the Presence of a Nuisance Change
• On the Properties of Gromov Matrices and their Applications in Network Inference
• Detection of Rumors and Their Sources in Social Networks: A Comprehensive Survey
• Optimal query complexity for private sequential learning against eavesdropping
• Estimating Infection Sources in Networks Using Partial Timestamps
• Anonymous Stochastic Routing
• Network Topology Inference Using Information Cascades with Limited Statistical Knowledge