Intrusion Detection in Unstructured Contexts Using On-line Clustering and Novelty Detection

dc.contributorpt-BR
dc.creatorAlves Ferreira, Eduardo
dc.creatorMello, Rodrigo Fernandes
dc.date2013-05-13
dc.date.accessioned2018-11-07T21:08:51Z
dc.date.available2018-11-07T21:08:51Z
dc.identifierhttps://seer.ufrgs.br/rita/article/view/rita_v20_n2_p155WesleyVol20Nr2_155
dc.identifier10.22456/2175-2745.26211
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/2187478
dc.descriptionThe characterization of processes behavior is usually considered whenperforming intrusion detection. Several works characterize specific aspects of systemsand attempt to detect novelties in that context, associating observed anomalies to at-tack events. Such approach is limited or even useless when the observed context isunstructured, i.e. when the monitor generates text-based log files or a variable numberof application attributes. In order to overcome such drawback, this paper considersthe use of single-pass clustering techniques to quantize unstructured data and generatetime series, using algorithms with low computational complexity, applicable in a real-world scenario. Afterward, novelty detection techniques are employed on such seriesto distinguish behavior anomalies, which are associated with intrusions. We evaluatedthe approach using a system characterization dataset and confirmed that it aggregatescontext information to represent the behavior of applications as time series, wherenovelty detection can be successfully performed.pt-BR
dc.formatapplication/pdf
dc.languagepor
dc.publisherInstituto de Informática - Universidade Federal do Rio Grande do Sulen-US
dc.relationhttps://seer.ufrgs.br/rita/article/view/rita_v20_n2_p155WesleyVol20Nr2_155/25444
dc.rightsDireitos autorais 2018 Eduardo Alves Ferreira, Rodrigo Fernandes Mellopt-BR
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0pt-BR
dc.sourceRevista de Informática Teórica e Aplicada; v. 20, n. 2 (2013); 155-173en-US
dc.sourceRevista de Informática Teórica e Aplicada; v. 20, n. 2 (2013); 155-173pt-BR
dc.source21752745
dc.source01034308
dc.subjectpt-BR
dc.titleIntrusion Detection in Unstructured Contexts Using On-line Clustering and Novelty Detectionpt-BR
dc.typeArtículos de revistas
dc.typeArtículos de revistas
dc.coveragept-BR
dc.coveragept-BR
dc.coveragept-BR


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