Zur Kurzanzeige

dc.contributor.authorAvdiienko, Vitalii
dc.contributor.authorKuznetsov, Konstantin
dc.contributor.authorGorla, Alessandra
dc.contributor.authorZeller, Andreas
dc.contributor.authorArzt, 
Steven
dc.contributor.authorRasthofer, Siegfried
dc.contributor.authorBodden, Eric
dc.contributor.editorJürjens, Jan
dc.contributor.editorSchneider, Kurt
dc.date.accessioned2017-06-21T19:18:09Z
dc.date.available2017-06-21T19:18:09Z
dc.date.issued2017
dc.identifier.isbn978-3-88579-661-9
dc.identifier.issn1617-5468
dc.description.abstractA fundamental question of security analysis is: When is a behavior normal, and when is it not? We present techniques that extract behavior patterns from thousands of apps—patters that represent normal behavior, such as “A travel app normally does not access stored text messages”. Combining data flow analysis with app descriptions and GUI data from both apps and their stores allows for massive machine learning, which then also allows to detect yet unknown malware by classifying it as abnormal.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftware Engineering 2017
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-267
dc.titleAppMiningen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages113
mci.conference.sessiontitleSecurity & Privacy
mci.conference.locationHannover
mci.conference.date21.-24. Februar 2017


Dateien zu dieser Ressource

Thumbnail

Zur Kurzanzeige