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On the Power of Adversarial Infections in Networks

  • Michael Brautbar
  • Moez Draief
  • Sanjeev Khanna
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8305)

Abstract

Over the last decade we have witnessed the rapid proliferation of online networks and Internet activity. Such activity is considered as a blessing but it brings with it a large increase in risk of computer malware — malignant software that actively spreads from one computer to another. To date, the majority of existing models of malware spread use stochastic behavior, when the set of neighbors infected from the current set of infected nodes is chosen obliviously. In this work, we initiate the study of adversarial infection strategies which can decide intelligently which neighbors of infected nodes to infect next in order to maximize their spread, while maintaining a similar “signature” as the oblivious stochastic infection strategy as not to be discovered. We first establish that computing an optimal and near-optimal adversarial strategies is computationally hard. We then identify necessary and sufficient conditions in terms of network structure and edge infection probabilities such that the adversarial process can infect polynomially more nodes than the stochastic process while maintaining a similar “signature” as the oblivious stochastic infection strategy. Among our results is a surprising connection between an additional structural quantity of interest in a network, the network toughness, and adversarial infections. Based on the network toughness, we characterize networks where existence of adversarial strategies that are pandemic (infect all nodes) is guaranteed, as well as efficiently computable.

Keywords

Span Tree Hamiltonian Cycle Independent Cascade Weighted Undirected Graph Adversary Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Michael Brautbar
    • 1
  • Moez Draief
    • 2
  • Sanjeev Khanna
    • 1
  1. 1.Computer and Information ScienceUniversity of PennsylvaniaUSA
  2. 2.Electrical and Electronic EngineeringImperial College LondonUK

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