Markov Random Fields for malware propagation: The case of chain networks

Karyotis, V

dc.contributor.author	Karyotis, V	en
dc.date.accessioned	2014-03-01T01:33:43Z
dc.date.available	2014-03-01T01:33:43Z
dc.date.issued	2010	en
dc.identifier.issn	1089-7798	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20557
dc.subject	chain networks	en
dc.subject	Gibbs sampling	en
dc.subject	malware propagation	en
dc.subject	Markov Random Fields	en
dc.subject.classification	Telecommunications	en
dc.subject.other	Chain networks	en
dc.subject.other	Dynamic behaviors	en
dc.subject.other	Gibbs sampling	en
dc.subject.other	Malware propagation	en
dc.subject.other	Malwares	en
dc.subject.other	Markov Random Fields	en
dc.subject.other	Wired and wireless	en
dc.subject.other	Image segmentation	en
dc.subject.other	Stochastic models	en
dc.subject.other	Computer crime	en
dc.title	Markov Random Fields for malware propagation: The case of chain networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/LCOMM.2010.072910.100866	en
heal.identifier.secondary	http://dx.doi.org/10.1109/LCOMM.2010.072910.100866	en
heal.identifier.secondary	5539722	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	Epidemic and stochastic models have been employed for describing the dynamic behavior of malware outbreaks. However, most of them lack a holistic treatment of the problem. In this work, we model malware propagation as a Markov Random Field and employ Gibbs sampling for the analysis of the system. We demonstrate the proposed framework for the case of a chain network, a model often emerging in both wired and wireless multi-hop networks. © 2010 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Communications Letters	en
dc.identifier.doi	10.1109/LCOMM.2010.072910.100866	en
dc.identifier.isi	ISI:000283370500031	en
dc.identifier.volume	14	en
dc.identifier.issue	9	en
dc.identifier.spage	875	en
dc.identifier.epage	877	en