Containing smartphone worm propagation with an influence maximization algorithm

Peng,S, Wu,M, Wang,G and Yu,S 2014, Containing smartphone worm propagation with an influence maximization algorithm, Computer Networks, vol. 74, no. Part B, pp. 103-113, doi: 10.1016/j.comnet.2014.09.004.

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Title Containing smartphone worm propagation with an influence maximization algorithm
Author(s) Peng,S
Yu,SORCID iD for Yu,S
Journal name Computer Networks
Volume number 74
Issue number Part B
Start page 103
End page 113
Total pages 11
Publisher Elsevier BV
Place of publication Amsterdam, Netherlands
Publication date 2014-12-09
ISSN 1389-1286
Keyword(s) Influence maximization
Social relationship graph
Voting algorithm Immunization
Worm containment
Science & Technology
Computer Science, Hardware & Architecture
Computer Science, Information Systems
Engineering, Electrical & Electronic
Computer Science
Voting algorithm
Summary In recent years, wide attention has been drawn to the problem of containing worm propagation in smartphones. Unlike existing containment models for worm propagation, we study how to prevent worm propagation through the immunization of key nodes (e.g.; the top k influential nodes). Thus, we propose a novel containment model based on an influence maximization algorithm. In this model, we introduce a social relation graph to evaluate the influence of nodes and an election mechanism to find the most influential nodes. Finally, this model provides a targeted immunization strategy to disable worm propagation by immunizing the top k influential nodes. The experimental results show that the model not only finds the most influential top k nodes quickly, but also effectively restrains and controls worm propagation.
Language eng
DOI 10.1016/j.comnet.2014.09.004
Field of Research 080503 Networking and Communications
Socio Economic Objective 890101 Fixed Line Data Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
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Citation counts: TR Web of Science Citation Count  Cited 11 times in TR Web of Science
Scopus Citation Count Cited 11 times in Scopus
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