Unsupervised Anomaly Detection with Unlabeled Data Using Clustering

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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...

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Bibliographic Details
Main Authors:	Chimphlee, Witcha, Abdullah, Abdul Hanan, Md. Sap, Mohd. Noor
Format:	Conference or Workshop Item
Language:	English
Published:	2005
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/3356/ http://eprints.utm.my/3356/1/Mohd_Noor_-_Unsupervised_Anomaly_Detection_with_Unlabeled_Data.pdf

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