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dc.contributor.authorLin, Ying-Daren_US
dc.contributor.authorLin, Po-Chingen_US
dc.contributor.authorWang, Sheng-Haoen_US
dc.contributor.authorChen, I-Weien_US
dc.contributor.authorLai, Yuan-Chengen_US
dc.date.accessioned2017-04-21T06:56:50Z-
dc.date.available2017-04-21T06:56:50Z-
dc.date.issued2016-06en_US
dc.identifier.issn1932-8184en_US
dc.identifier.urihttp://dx.doi.org/10.1109/JSYST.2014.2301464en_US
dc.identifier.urihttp://hdl.handle.net/11536/132579-
dc.description.abstractThis paper presents the PCAPLib system for providing extracted, well-classified, and anonymized packet traces from real network traffic with two mechanisms. First, active trace collection actively extracts and classifies packet traces into sessions by leveraging multiple detection devices. Second, deep packet anonymization protects the privacy in the packet payloads for hundreds of application protocols while preserving the utility of the traces. We evaluate 318 anonymized packet traces collected over a period of four months and show that the efficiency of anonymization is up to 96%. The usefulness of this system for assessing false positives/false negatives in intrusion detection has been also demonstrated.en_US
dc.language.isoen_USen_US
dc.subjectPacket anonymizationen_US
dc.subjectprivacyen_US
dc.subjecttrace repositoryen_US
dc.subjectutilityen_US
dc.titlePCAPLib: A System of Extracting, Classifying, and Anonymizing Real Packet Tracesen_US
dc.identifier.doi10.1109/JSYST.2014.2301464en_US
dc.identifier.journalIEEE SYSTEMS JOURNALen_US
dc.citation.volume10en_US
dc.citation.issue2en_US
dc.citation.spage520en_US
dc.citation.epage531en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department網路測試中心zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentNetwork Benchmarking Laben_US
dc.identifier.wosnumberWOS:000383258600013en_US
Appears in Collections:Articles