Passive Fingerprinting of Known Operating Systems using Deep Learning Techniques

dc.contributor.author	Løland, Martin Veshovda
dc.date.accessioned	2019-09-11T23:45:47Z
dc.date.available	2019-09-11T23:45:47Z
dc.date.issued	2019
dc.identifier.citation	Løland, Martin Veshovda. Passive Fingerprinting of Known Operating Systems using Deep Learning Techniques. Master thesis, University of Oslo, 2019
dc.identifier.uri	http://hdl.handle.net/10852/70346
dc.description.abstract	Passive fingerprinting with a Deep Learning approach. The approach is compared to three well established machine learning algorithms; SVM, KNN and Random Forest. A never before used value for fingerprinting, the TCP Congestion Control Algorithm, was evaluated as a feature.	eng
dc.language.iso	eng
dc.subject	TCP Congestion Avoidance
dc.subject	Fingerprinting
dc.subject	Passive Fingerprinting
dc.subject	TCP Variant
dc.subject	Operating System
dc.subject	Machine Learning
dc.subject	Deep Learning
dc.title	Passive Fingerprinting of Known Operating Systems using Deep Learning Techniques	eng
dc.type	Master thesis
dc.date.updated	2019-09-11T23:45:47Z
dc.creator.author	Løland, Martin Veshovda
dc.identifier.urn	URN:NBN:no-73477
dc.type.document	Masteroppgave
dc.identifier.fulltext	Fulltext https://www.duo.uio.no/bitstream/handle/10852/70346/1/Passive_Fingerprinting_of_Known_Operating_Systems_Using_Deep_Learning_Techniques_Martin_V_Loeland.pdf

Browse