A Nature-inspired Approach To Speed Up Optimum-path Forest Clustering And Its Application To Intrusion Detection In Computer Networks

dc.creatorCosta
dc.creatorKelton A. P.; Pereira
dc.creatorLuis A. M.; Nakamura
dc.creatorRodrigo Y. M.; Pereira
dc.creatorClayton R.; Papa
dc.creatorJoao P.; Falcao
dc.creatorAlexandre Xavier
dc.date2015-FEB
dc.date2016-06-07T13:13:33Z
dc.date2016-06-07T13:13:33Z
dc.date.accessioned2018-03-29T01:34:35Z
dc.date.available2018-03-29T01:34:35Z
dc.identifier
dc.identifierA Nature-inspired Approach To Speed Up Optimum-path Forest Clustering And Its Application To Intrusion Detection In Computer Networks. Elsevier Science Inc, v. 294, p. 95-108 FEB-2015.
dc.identifier0020-0255
dc.identifierWOS:000346542800008
dc.identifier10.1016/j.ins.2014.09.025
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S0020025514009311
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/241501
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1305199
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionWe propose a nature-inspired approach to estimate the probability density function (pdf) used for data clustering based on the optimum-path forest algorithm (OPFC). OPFC interprets a dataset as a graph, whose nodes are the samples and each sample is connected to its k-nearest neighbors in a given feature space (a k-nn graph). The nodes of the graph are weighted by their pdf values and the pdf is computed based on the distances between the samples and their k-nearest neighbors. Once the k-nn graph is defined, OPFC finds one sample (root) at each maximum of the pdf and propagates one optimum-path tree (cluster) from each root to the remaining samples of its dome. Clustering effectiveness will depend on the pdf estimation, and the proposed approach efficiently computes the best value of k for a given application. We validate our approach in the context of intrusion detection in computer networks. First, we compare OPFC with data clustering based on k-means, and self-organization maps. Second, we evaluate several metaheuristic techniques to find the best value of k. (C) 2014 Elsevier Inc. All rights reserved.
dc.description294
dc.description
dc.description
dc.description95
dc.description108
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFAPESP [2009/16206-1, 2010/02045-3, 2011/14058-5, 2011/14094-1, 2013/20387-7]
dc.descriptionCNPq [303673/2010-9, 3031821/2011-3, 470571/2013-6]
dc.description
dc.description
dc.description
dc.languageen
dc.publisherELSEVIER SCIENCE INC
dc.publisher
dc.publisherNEW YORK
dc.relationINFORMATION SCIENCES
dc.rightsembargo
dc.sourceWOS
dc.subjectImage Segmentation
dc.subjectDetection System
dc.subjectMisuse Detection
dc.subjectBat Algorithm
dc.subjectOptimization
dc.subjectClassification
dc.subjectCloud
dc.titleA Nature-inspired Approach To Speed Up Optimum-path Forest Clustering And Its Application To Intrusion Detection In Computer Networks
dc.typeArtículos de revistas


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