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dc.contributor.authorBodström, Tero
dc.contributor.authorHämäläinen, Timo
dc.date.accessioned2019-04-04T08:23:06Z
dc.date.available2019-04-04T08:23:06Z
dc.date.issued2019
dc.identifier.citationBodström, T., & Hämäläinen, T. (2019). A Novel Deep Learning Stack for APT Detection. <i>Applied Sciences</i>, <i>9</i>(6), Article 1055. <a href="https://doi.org/10.3390/app9061055" target="_blank">https://doi.org/10.3390/app9061055</a>
dc.identifier.otherCONVID_28975218
dc.identifier.otherTUTKAID_80987
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/63384
dc.description.abstractWe present a novel Deep Learning (DL) stack for detecting Advanced Persistent threat (APT) attacks. This model is based on a theoretical approach where an APT is observed as a multi-vector multi-stage attack with a continuous strategic campaign. To capture these attacks, the entire network flow and particularly raw data must be used as an input for the detection process. By combining different types of tailored DL-methods, it is possible to capture certain types of anomalies and behaviour. Our method essentially breaks down a bigger problem into smaller tasks, tries to solve these sequentially and finally returns a conclusive result. This concept paper outlines, for example, the problems and possible solutions for the tasks. Additionally, we describe how we will be developing, implementing and testing the method in the near future.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherMDPI AG
dc.relation.ispartofseriesApplied Sciences
dc.rightsCC BY 4.0
dc.subject.otherAdvanced Persistent Thread (APT)
dc.subject.otherDeep Learning (DL)
dc.subject.othernetwork anomaly detection
dc.titleA Novel Deep Learning Stack for APT Detection
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201903201916
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2019-03-20T10:15:17Z
dc.description.reviewstatuspeerReviewed
dc.relation.issn2076-3417
dc.relation.numberinseries6
dc.relation.volume9
dc.type.versionpublishedVersion
dc.rights.copyright© 2019 by The Authors. Licensee MDPI, Basel, Switzerland.
dc.rights.accesslevelopenAccessfi
dc.subject.ysotietoturva
dc.subject.ysoverkkohyökkäykset
dc.subject.ysokoneoppiminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p5479
jyx.subject.urihttp://www.yso.fi/onto/yso/p27466
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3390/app9061055


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