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dc.contributor.authorHammer, Hugo Lewien_US
dc.contributor.authorKongsgård, Kyrre Wahlen_US
dc.contributor.authorYazidi, Anisen_US
dc.contributor.authorBai, Aleksanderen_US
dc.contributor.authorNordbotten, Nils Agneen_US
dc.contributor.authorEngelstad, Paal E.en_US
dc.date.accessioned2016-03-22T09:12:27Z
dc.date.available2016-03-22T09:12:27Z
dc.date.issued2015en_US
dc.identifier.citationHammer, H.L., Kongsgård, K.W., Yazidi, A., Bai, A., Nordbotten, N.A. & Engelstad, P.E. (2015). Automatic security classification by machine learning for cross-domain information exchange. MILCOM IEEE Military Communications Conference. doi: 10.1109/MILCOM.2015.7357672en_US
dc.identifier.issn2155-7578en_US
dc.identifier.otherFRIDAID 1294371en_US
dc.identifier.urihttps://hdl.handle.net/10642/3199
dc.description.abstractCross-domain information exchange is necessary to obtain information superiority in the military domain, and should be based on assigning appropriate security labels to the information objects. Most of the data found in a defense network is unlabeled, and usually new unlabeled information is produced every day. Humans find that doing the security labeling of such information is labor-intensive and time consuming. At the same time there is an information explosion observed where more and more unlabeled information is generated year by year. This calls for tools that can do advanced content inspection, and automatically determine the security label of an information object correspondingly. This paper presents a machine learning approach to this problem. To the best of our knowledge, machine learning has hardly been analyzed for this problem, and the analysis on topical classification presented here provides new knowledge and a basis for further work within this area. Presented results are promising and demonstrates that machine learning can become a useful tool to assist humans in determining the appropriate security label of an information objecten_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartofseriesMILCOM IEEE Military Communications Conference;en_US
dc.subjectSecurityen_US
dc.subjectClassificationen_US
dc.subjectLabelingen_US
dc.subjectMachine learningen_US
dc.subjectCross-domain information exchangeen_US
dc.titleAutomatic security classification by machine learning for cross-domain information exchangeen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.identifier.doihttp://dx.doi.org/10.1109/MILCOM.2015.7357672


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