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dc.contributor.advisorAlouini, Mohamed-Slim
dc.contributor.authorZhaikhan, Ainur
dc.date.accessioned2020-04-21T13:26:16Z
dc.date.available2021-04-20T00:00:00Z
dc.date.issued2020-04
dc.identifier.citationZhaikhan, A. (2020). Percolation Theory-Analysis of Malware Epidemics in Large-Scale Wireless Networks. KAUST Research Repository. https://doi.org/10.25781/KAUST-V5ZC3
dc.identifier.doi10.25781/KAUST-V5ZC3
dc.identifier.urihttp://hdl.handle.net/10754/662603
dc.description.abstractThe foreseen massive deployment of the internet of things (IoT) is expected to suffer from high security risks. This mainly results from the difficulty to monitor and cure the IoT devices in such large-scale deployment. In this thesis, we propose a spatial random deployment of special nodes (firewalls) which can detect and cure infected nodes within certain radius. An important concern is to add sufficient number of firewalls to make an epidemics finite and, hence, prevent malware outbreak over the whole network. The problem will be analyzed using percolation theory. Namely, we derive an upperbound for the critical intensity of spatial firewalls which guarantees prevention of large-scale network epidemics, regardless of the intensity of regular nodes. Using tools from percolation theory, we analyze the proposed solution and show the conditions required to ensure its efficiency.
dc.language.isoen
dc.subjectnetwork epidemics
dc.subjectpercolation theory
dc.subjectrandom graphs
dc.subjectwireless
dc.subjectcommunication network
dc.titlePercolation Theory-Analysis of Malware Epidemics in Large-Scale Wireless Networks
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.rights.embargodate2021-04-20
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberShihada, Basem
dc.contributor.committeememberAmin, Osama
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.nameMaster of Science
dc.rights.accessrightsAt the time of archiving, the student author of this thesis opted to temporarily restrict access to it. The full text of this thesis became available to the public after the expiration of the embargo on 2021-04-20.
kaust.request.doiyes


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