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Conference Paper: A Coarse-to-Fine Grained Knowledge Refinement Framework for Network Intrusion Detection System

TitleA Coarse-to-Fine Grained Knowledge Refinement Framework for Network Intrusion Detection System
Authors
Tam, Wai Leuk Vincent LI, ZhenglongYeung, Lawrence Kwan
KeywordsDomain Knowledge Learning
Network Intrusion Detection
Representation Learning
Issue Date4-Nov-2022
Abstract

To detect cyberattacks, a coarse-to-fine grained representation framework is proposed through refining the knowledge from different grain levels, and then demonstrates impressive enhancements on anormal detection, especially on discovering unknown attacks.


Persistent Identifierhttp://hdl.handle.net/10722/339484

 

DC FieldValueLanguage
dc.contributor.authorTam, Wai Leuk Vincent -
dc.contributor.authorLI, Zhenglong-
dc.contributor.authorYeung, Lawrence Kwan-
dc.date.accessioned2024-03-11T10:37:00Z-
dc.date.available2024-03-11T10:37:00Z-
dc.date.issued2022-11-04-
dc.identifier.urihttp://hdl.handle.net/10722/339484-
dc.description.abstract<p>To detect cyberattacks, a coarse-to-fine grained representation framework is proposed through refining the knowledge from different grain levels, and then demonstrates impressive enhancements on anormal detection, especially on discovering unknown attacks.<br></p>-
dc.languageeng-
dc.relation.ispartof2022 IEEE Region 10 Conference (TENCON) (01/11/2022-04/11/2022, Hong Kong)-
dc.subjectDomain Knowledge Learning-
dc.subjectNetwork Intrusion Detection-
dc.subjectRepresentation Learning-
dc.titleA Coarse-to-Fine Grained Knowledge Refinement Framework for Network Intrusion Detection System-
dc.typeConference_Paper-
dc.identifier.doi10.1109/TENCON55691.2022.9978163-
dc.identifier.scopuseid_2-s2.0-85145662638-
dc.identifier.volume2022-November-
dc.identifier.spage1-
dc.identifier.epage6-

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