Online anomaly detection using KDE

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bracu.type.groupResearch Publications
datacite.rightsMetadata Only
dc.contributor.authorAhmed, Tarem
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2026-08-23T08:03:09Z
dc.date.available2026-08-23T08:03:09Z
dc.date.issued2009-12-01
dc.description.abstractLarge backbone networks are regularly affected by a range of anomalies. This paper presents an online anomaly detection algorithm based on Kernel Density Estimates. The proposed algorithm sequentially and adaptively learns the definition of normality in the given application, assumes no prior knowledge regarding the underlying distributions, and then detects anomalies subject to a user-set tolerance level for false alarms. Comparison with the existing methods of Geometric Entropy Minimization, Principal Component Analysis and One-Class Neighbor Machine demonstrates that the proposed method achieves superior performance with lower complexity.
dc.description.versionPublished
dc.identifier.citationT. Ahmed, "Online Anomaly Detection Using KDE," GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, Honolulu, HI, USA, 2009, pp. 1-8, doi: 10.1109/GLOCOM.2009.5425504.
dc.identifier.doi10.1109/GLOCOM.2009.5425504
dc.identifier.issn9781424441488
dc.identifier.other2-s2.0-77951570750
dc.identifier.urihttps://hdl.handle.net/10361/29457
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.hasversion10.1109/GLOCOM.2009.5425504
dc.relation.ispartofGlobecom IEEE Global Telecommunications Conference
dc.relation.ispartofseriesGlobecom IEEE Global Telecommunications Conference
dc.relation.urihttps://ieeexplore.ieee.org/document/5425504
dc.subjectMachine learning algorithms
dc.subjectEntropy
dc.subjectMinimization methods
dc.subjectSpine
dc.subjectDetection algorithms
dc.subjectApplication software
dc.subjectPrincipal component analysis
dc.subjectHigh-speed networks
dc.subject.lcshAnomaly detection (Computer security).
dc.subject.lcshComputer networks--Monitoring.
dc.titleOnline anomaly detection using KDE
dc.typeConference Proceeding
person.affiliation.nameBRAC University
person.identifier.scopus-author-id20435549100

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