A Detailed Analysis of the KDD CUP 99 Data Set

dc.contributor.author	O’rinov Nodirbek Toxirjonovich
dc.contributor.author	Foziljonova Marxabo
dc.date.accessioned	2026-01-01T21:15:24Z
dc.date.issued	2022-11-30
dc.description.abstract	During in last decade, anomaly detection It has attracted the attention of many researchers to overcome the weakness of signature-based IDS in detecting new attacks, and KDDCUP'99 is the most widely used dataset for grade from these systems. Having held a statistical analysis of this data set, we found two important issues that greatly affects the performance of the systems being evaluated, and the resultsin a very poor evaluation of anomaly detection approaches. To to solve these problems, we proposed a new dataset, NSL-KDD, which the consists from selected records from in full KDD datainstalled as well as does No suffer from Any from mentioned limitations
dc.format	application/pdf
dc.identifier.uri	https://geniusjournals.org/index.php/erb/article/view/2730
dc.identifier.uri	https://asianeducationindex.com/handle/123456789/66865
dc.language.iso	eng
dc.publisher	Genius Journals
dc.relation	https://geniusjournals.org/index.php/erb/article/view/2730/2340
dc.source	Eurasian Research Bulletin ; Vol. 14 (2022): ERB; 290-300
dc.source	2795-7675
dc.subject	approaches
dc.subject	anomaly
dc.subject	attracted
dc.subject	researchers
dc.title	A Detailed Analysis of the KDD CUP 99 Data Set
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Peer-reviewed Article

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