Show simple item record

dc.contributor.authorRenners, Leonard
dc.contributor.editorMeier, Michael
dc.contributor.editorReinhardt, Delphine
dc.contributor.editorWendzel, Steffen
dc.date.accessioned2017-06-21T07:43:29Z
dc.date.available2017-06-21T07:43:29Z
dc.date.issued2016
dc.identifier.isbn978-3-88579-650-3
dc.identifier.issn1617-5468
dc.description.abstractIn the network security domain Intrusion detection systems (IDS) are known for their problems in creating huge amounts of data and especially false positives. Several approaches, originating in the machine learning domain, have been proposed for a better classification. However, threat prioritization has also shown, that a distinction in true and false positives is not always sufficient for a profound security analysis. We therefore propose an approach to combine several aspects from those two areas. On the one hand, threat and event prioritization approaches are rather static with fixed calculation rules, whereas rule learning in alert verification focuses mostly on a binaryen
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSicherheit 2016 - Sicherheit, Schutz und Zuverlässigkeit
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-256
dc.titleTowards adaptive event prioritization for network security - ideas and challengesen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages197-202
mci.conference.locationBonn
mci.conference.date5.-7. April 2016


Files in this item

Thumbnail

Show simple item record