Yuan, Yali

[["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","International Journal of Distributed Sensor Networks"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Yuan, Yali"],["dc.contributor.author","Huo, Liuwei"],["dc.contributor.author","Yuan, Yachao"],["dc.contributor.author","Wang, Zhixiao"],["dc.date.accessioned","2020-12-10T18:38:33Z"],["dc.date.available","2020-12-10T18:38:33Z"],["dc.date.issued","2019"],["dc.description.abstract","Network intrusion detection is a relatively mature research topic, but one that remains challenging particular as technologies and threat landscape evolve. Here, a semi-supervised tri-Adaboost (STA) algorithm is proposed. In the algorithm, three different Adaboost algorithms are used as the weak classifiers (both for continuous and categorical data), constituting the decision stumps in the tri-training method. In addition, the chi-square method is used to reduce the dimension of feature and improve computational efficiency. We then conduct extensive numerical studies using different training and testing samples in the KDDcup99 dataset and discover the flows demonstrated that (1) high accuracy can be obtained using a training dataset which consists of a small number of labeled and a large number of unlabeled samples. (2) The algorithm proposed is reproducible and consistent over different runs. (3) The proposed algorithm outperforms other existing learning algorithms, even with only a small amount of labeled data in the training phase. (4) The proposed algorithm has a short execution time and a low false positive rate, while providing a desirable detection rate."],["dc.identifier.doi","10.1177/1550147719846052"],["dc.identifier.eissn","1550-1477"],["dc.identifier.issn","1550-1477"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16456"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77369"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Gesellschaft für wissenschaftliche Datenverarbeitung"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Semi-supervised tri-Adaboost algorithm for network intrusion detection"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]