A differential game approach to decentralized virus-resistant weight adaptation policy over complex networks

Yunhan Huang, Quanyan Zhu

Research output: Contribution to journalArticlepeer-review

Abstract

Increasing connectivity of communication networks enables large-scale distributed processing over networks and improves the efficiency of information exchange. However, malware and a virus can take advantage of the high connectivity to spread over the network and take control of devices and servers for illicit purposes. In this paper, we use a susceptible-infected-susceptible epidemic model to capture the virus spreading process and develop a virus-resistant weight adaptation scheme to mitigate the spreading over the network. We propose a differential game framework to provide a theoretic underpinning for decentralized mitigation in which nodes of the network cannot fully coordinate, and each node determines its own control policy based on local interactions with neighboring nodes. We characterize and examine the structure of the Nash equilibrium, and discuss the inefficiency of the Nash equilibrium in terms of minimizing the total cost of the whole network. A mechanism design through a penalty scheme is proposed to reduce the inefficiency of the Nash equilibrium and allow the decentralized policy to achieve social welfare for the whole network. We corroborate our results using numerical experiments and show that virus resistance can be achieved by a distributed weight adaptation scheme.

Original languageEnglish (US)
Article number8779673
Pages (from-to)944-955
Number of pages12
JournalIEEE Transactions on Control of Network Systems
Volume7
Issue number2
DOIs
StatePublished - Jun 2020

Keywords

  • Complex networks
  • decentralized control
  • differential games
  • epidemic processes
  • malware spreading
  • mechanism design
  • network security
  • virus resistance

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Control and Optimization

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