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dc.contributor.authorPorter, Kyle
dc.date.accessioned2019-03-22T14:45:11Z
dc.date.available2019-03-22T14:45:11Z
dc.date.created2018-10-30T11:14:21Z
dc.date.issued2018
dc.identifier.citationDigital Investigation. The International Journal of Digital Forensics and Incident Response. 2018, 26 S87-S97.nb_NO
dc.identifier.issn1742-2876
dc.identifier.urihttp://hdl.handle.net/11250/2591393
dc.description.abstractDarknet markets, which can be considered as online black markets, in general sell illegal items such as drugs, firearms, and malware. In July 2017, significant law enforcement operations compromised or completely took down multiple international darknet markets. To quickly understand how this affected the markets and the choice of tools utilized by users of darknet markets, we use unsupervised topic modeling techniques on the DarkNetMarkets subreddit in order to determine prominent topics and terms, and how they have changed over a year's time. After extracting, filtering out irrelevant posts, and preprocessing the text crawled from the subreddit, we perform Latent Dirichlet Allocation (LDA) topic modeling on a corpus of posts for each month from November 5th, 2016 to November 5th, 2017. Our results indicate that even analyzing public forums such as the DarkNetMarkets subreddit can reveal trends and keywords related to criminal activity and their methods, and that users of the dark web appear to be becoming increasingly more security-minded due to the recent law enforcement events.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleAnalyzing the DarkNetMarkets subreddit for evolutions of tools and trends using LDA topic modelingnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumberS87-S97nb_NO
dc.source.volume26nb_NO
dc.source.journalDigital Investigation. The International Journal of Digital Forensics and Incident Responsenb_NO
dc.identifier.doi10.1016/j.diin.2018.04.023
dc.identifier.cristin1624844
dc.description.localcode© 2018 The Author(s). Published by Elsevier Ltd on behalf of DFRWS. This is an open access article under the CC BY-NC-ND license (http://creativecommon s.org/ licenses/by-nc-nd/4.0/).nb_NO
cristin.unitcode194,63,30,0
cristin.unitnameInstitutt for informasjonssikkerhet og kommunikasjonsteknologi
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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