Noisy mean field game model for malware propagation in opportunistic networks

Handle URI:
http://hdl.handle.net/10754/575762
Title:
Noisy mean field game model for malware propagation in opportunistic networks
Authors:
Tembine, Hamidou; Vilanova, Pedro ( 0000-0001-6620-6261 ) ; Debbah, Méroúane
Abstract:
In this paper we present analytical mean field techniques that can be used to better understand the behavior of malware propagation in opportunistic large networks. We develop a modeling methodology based on stochastic mean field optimal control that is able to capture many aspects of the problem, especially the impact of the control and heterogeneity of the system on the spreading characteristics of malware. The stochastic large process characterizing the evolution of the total number of infected nodes is examined with a noisy mean field limit and compared to a deterministic one. The stochastic nature of the wireless environment make stochastic approaches more realistic for such types of networks. By introducing control strategies, we show that the fraction of infected nodes can be maintained below some threshold. In contrast to most of the existing results on mean field propagation models which focus on deterministic equations, we show that the mean field limit is stochastic if the second moment of the number of object transitions per time slot is unbounded with the size of the system. This allows us to compare one path of the fraction of infected nodes with the stochastic trajectory of its mean field limit. In order to take into account the heterogeneity of opportunistic networks, the analysis is extended to multiple types of nodes. Our numerical results show that the heterogeneity can help to stabilize the system. We verify the results through simulation showing how to obtain useful approximations in the case of very large systems. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
Springer Science + Business Media
Journal:
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Conference/Event name:
2nd International ICST Conference on Game Theory in Networks, GAMENETS 2011
Issue Date:
2012
DOI:
10.1007/978-3-642-30373-9_32
Type:
Conference Paper
ISSN:
18678211
ISBN:
9783642303722
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorTembine, Hamidouen
dc.contributor.authorVilanova, Pedroen
dc.contributor.authorDebbah, Méroúaneen
dc.date.accessioned2015-08-24T09:25:31Zen
dc.date.available2015-08-24T09:25:31Zen
dc.date.issued2012en
dc.identifier.isbn9783642303722en
dc.identifier.issn18678211en
dc.identifier.doi10.1007/978-3-642-30373-9_32en
dc.identifier.urihttp://hdl.handle.net/10754/575762en
dc.description.abstractIn this paper we present analytical mean field techniques that can be used to better understand the behavior of malware propagation in opportunistic large networks. We develop a modeling methodology based on stochastic mean field optimal control that is able to capture many aspects of the problem, especially the impact of the control and heterogeneity of the system on the spreading characteristics of malware. The stochastic large process characterizing the evolution of the total number of infected nodes is examined with a noisy mean field limit and compared to a deterministic one. The stochastic nature of the wireless environment make stochastic approaches more realistic for such types of networks. By introducing control strategies, we show that the fraction of infected nodes can be maintained below some threshold. In contrast to most of the existing results on mean field propagation models which focus on deterministic equations, we show that the mean field limit is stochastic if the second moment of the number of object transitions per time slot is unbounded with the size of the system. This allows us to compare one path of the fraction of infected nodes with the stochastic trajectory of its mean field limit. In order to take into account the heterogeneity of opportunistic networks, the analysis is extended to multiple types of nodes. Our numerical results show that the heterogeneity can help to stabilize the system. We verify the results through simulation showing how to obtain useful approximations in the case of very large systems. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.en
dc.publisherSpringer Science + Business Mediaen
dc.titleNoisy mean field game model for malware propagation in opportunistic networksen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineeringen
dc.conference.date16 April 2011 through 18 April 2011en
dc.conference.name2nd International ICST Conference on Game Theory in Networks, GAMENETS 2011en
dc.conference.locationShanghaien
kaust.authorTembine, Hamidouen
kaust.authorVilanova, Pedroen
kaust.authorDebbah, Méroúaneen
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