Detection of Malicious Network Traffic Behavior Using JA3 Fingerprints

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but.event.date26.04.2022cs
but.event.titleSTUDENT EEICT 2022cs
dc.contributor.authorNovák, Pavel
dc.contributor.authorOujezský, Václav
dc.date.accessioned2022-12-06T13:21:59Z
dc.date.available2022-12-06T13:21:59Z
dc.date.issued2022cs
dc.description.abstractThis paper presents a novel approach for classifying spoof network traffic based on JA3 fingerprint clustering. In particular, it concerns the detection of so-called zero-day malware. The proposed method does not work with known JA3 hashes. However, it compares the JA3 fingerprint of captured traffic with JA3 fingerprints of traffic with predefined criteria, such as the use of current cipher suites or protocol, for classification.en
dc.formattextcs
dc.format.extent194-197cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 28st Conference STUDENT EEICT 2022: Selected papers. s. 194-197. ISBN 978-80-214-6030-0cs
dc.identifier.doi10.13164/eeict.2022.194
dc.identifier.isbn978-80-214-6030-0
dc.identifier.urihttp://hdl.handle.net/11012/208635
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 28st Conference STUDENT EEICT 2022: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectclustering, detection, JA3, JA3s, malwareen
dc.titleDetection of Malicious Network Traffic Behavior Using JA3 Fingerprintsen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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