Change Detection in Large Evolving Networks

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dc.contributor.authorNamayanja, Josephine M.
dc.contributor.authorJaneja, Vandana P.
dc.date.accessioned2019-04-17T18:56:37Z
dc.date.available2019-04-17T18:56:37Z
dc.date.issued2019-04
dc.description.abstractThis article presents a novel technique for the detection of change in massive evolving communication networks. This approach utilizes a novel hybrid sampling methodology to select central nodes and key subgraphs from networks over time. The objective is to select and utilize a much smaller targeted sample of the network, represented as a graph, without loss of any knowledge derived from graph properties as compared to the entire massive graph. This article uses the targeted samples to detect micro- and macro-level changes in the network. This approach can be potentially useful in the domain of cybersecurity where this article highlights the importance of graph sampling and multi-level change detection in identifying network changes that may be difficult to detect on a larger scale. This article therefore presents a means to audit large networks to establish continuous awareness of network behavior.en_US
dc.description.sponsorshipResearch is funded through federal, state and private organizations including NSF, U.S. Army Corps of Engineers, MD State Highway Administrationen_US
dc.description.urihttps://www.igi-global.com/article/change-detection-in-large-evolving-networks/225807en_US
dc.format.extent18 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2tlhq-5iyx
dc.identifier.citationJosephine M. Namayanja, Vandana P. Janeja, Change Detection in Large Evolving Networks, International Journal of Data Warehousing and Mining, Volume 15 , Issue 2 ,April-June 2019, DOI: 10.4018/IJDWM.2019040104en_US
dc.identifier.urihttps://doi.org/10.4018/IJDWM.2019040104
dc.identifier.urihttp://hdl.handle.net/11603/13444
dc.language.isoen_USen_US
dc.publisherIGI Globalen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rights© IGI Global
dc.subjectBig Dataen_US
dc.subjectcentral nodesen_US
dc.subjectcyber threatsen_US
dc.subjecthybrid samplingen_US
dc.subjectkey subgraphsen_US
dc.subjectmacro-level changesen_US
dc.subjectmicro-level changesen_US
dc.subjecttemporal binningen_US
dc.subjectUMBC High Performance Computing Facility (HPCF)
dc.titleChange Detection in Large Evolving Networksen_US
dc.typeTexten_US

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