Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

dc.contributorUniversidade Estadual Paulista (UNESP)
dc.creatorBreve, Fabricio
dc.creatorZhao, Liang
dc.creatorEngelbrecht, A.
dc.creatorFilho, CJAB
dc.creatorNeto, FBD
dc.date2015-03-18T15:55:03Z
dc.date2016-10-25T20:32:43Z
dc.date2015-03-18T15:55:03Z
dc.date2016-10-25T20:32:43Z
dc.date2013-01-01
dc.date.accessioned2017-04-06T07:12:50Z
dc.date.available2017-04-06T07:12:50Z
dc.identifier2013 1st Brics Countries Congress On Computational Intelligence And 11th Brazilian Congress On Computational Intelligence (brics-cci & Cbic). New York: Ieee, p. 335-340, 2013.
dc.identifierhttp://hdl.handle.net/11449/117068
dc.identifierhttp://acervodigital.unesp.br/handle/11449/117068
dc.identifier10.1109/BRICS-CCI-CBIC.2013.63
dc.identifierWOS:000346422500052
dc.identifier0000-0002-1123-9784
dc.identifierhttp://dx.doi.org/10.1109/BRICS-CCI-CBIC.2013.63
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/927715
dc.descriptionConcept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.
dc.languageeng
dc.publisherIeee
dc.relation2013 1st Brics Countries Congress On Computational Intelligence And 11th Brazilian Congress On Computational Intelligence (brics-cci & Cbic)
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.titleSemi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data
dc.typeOtro


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