Unsupervised Anomaly Detection with Unlabeled Data Using Clustering
Intrusions pose a serious security risk in a network environment. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. Traditiona...
| Principais autores: | , , |
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| Formato: | Conference or Workshop Item |
| Idioma: | English |
| Publicado em: |
2005
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| Assuntos: | |
| Acesso em linha: | http://eprints.utm.my/3356/1/Mohd_Noor_-_Unsupervised_Anomaly_Detection_with_Unlabeled_Data.pdf |