Näytä suppeat kuvailutiedot

dc.contributor.authorJuvonen, Antti
dc.date.accessioned2014-11-27T10:52:14Z
dc.date.available2014-11-27T10:52:14Z
dc.date.issued2014
dc.identifier.isbn978-951-39-5978-4
dc.identifier.otheroai:jykdok.linneanet.fi:1451973
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/44755
dc.format.extent1 verkkoaineisto (57, [52] sivua)
dc.language.isoeng
dc.publisherUniversity of Jyväskylä
dc.relation.ispartofseriesJyväskylä studies in computing
dc.relation.haspart<b>Article I: </b> Tuomo Sipola, Antti Juvonen and Joel Lehtonen. Anomaly detection from network logs using diffusion maps. <i> Engineering Applications of Neural Networks, IFIP Advances in Information and Communication Technology, Vol. 363, pp. 172–181, 2011 </i> <a href=" http://urn.fi/URN:NBN:fi:jyu-201206051800 ">.Full text</a>
dc.relation.haspart<b>Article II: </b> Tuomo Sipola, Antti Juvonen and Joel Lehtonen. Dimensionality reduction framework for detecting anomalies from network logs. <i>Engineering Intelligent Systems, Vol. 20, Iss. 1–2, pp. 87–97, 2012. </i> <a href=" http://urn.fi/URN:NBN:fi:jyu-201210122663 ">Full text</a>
dc.relation.haspart<b>Article III: </b> Antti Juvonen and Tuomo Sipola. Adaptive framework for network traffic classification using dimensionality reduction and clustering. <i>Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2012 4th International Congress on, pp. 274–279, 2012. </i> <a href="http://urn.fi/URN:NBN:fi:jyu-201304121436 ">Full text</a>
dc.relation.haspart<b>Article IV: </b> Mikhail Zolotukhin, Timo Hämäläinen and Antti Juvonen. Growing hierarchical self-organizing maps and statistical distribution models for online detecion of web attacks. <i>Web Information Systems and Technologies. Lecture Notes in Business Information Processing, Vol. 140, pp. 281–295, 2013. </i><a href=" http://dx.doi.org/ 10.1007/978-3-642-36608-6_18 ">DOI: 10.1007/978-3-642-36608-6_18 </a>
dc.relation.haspart<b>Article V: </b> Antti Juvonen and Tuomo Sipola. Combining conjunctive rule extraction with diffusion maps for network intrusion detection. <i>The Eighteenth IEEE Symposium on Computers and Communications (ISCC 2013), pp. 411–416, 2013.</i><a href="http://urn.fi/URN:NBN:fi:jyu-201404031456 "> Full text</a>.
dc.relation.haspart<b>Article VI: </b> Antti Juvonen and Timo Hämäläinen. An efficient network log anomaly detection system using random projection dimensionality reduction. <i>New Technologies, Mobility and Security (NTMS), 2014 6th International Conference on, 2014.</i><a href="http://urn.fi/URN:NBN:fi:jyu-201406252141 "> Full text</a>
dc.relation.haspart<b>ArticleVII: </b> Antti Juvonen, Tuomo Sipola and Timo Hämäläinen. Online anomaly detection using dimensionality reduction techniques for http log analysis. <i>Submitted to Computer Networks, Elsevier, 2014.</i>
dc.subject.otherknowledge discovery
dc.subject.otherdata mining
dc.subject.otherintrusion detection
dc.subject.otheranomaly detection
dc.subject.otherdimensionality reduction
dc.subject.otherclustering
dc.titleIntrusion detection applications using knowledge discovery and data mining
dc.typeDiss.fi
dc.identifier.urnURN:ISBN:978-951-39-5978-4
dc.type.dcmitypeTexten
dc.type.ontasotVäitöskirjafi
dc.type.ontasotDoctoral dissertationen
dc.contributor.tiedekuntaInformaatioteknologian tiedekuntafi
dc.contributor.yliopistoUniversity of Jyväskyläen
dc.contributor.yliopistoJyväskylän yliopistofi
dc.contributor.oppiaineTietotekniikkafi
dc.relation.issn1456-5390
dc.relation.numberinseries205
dc.rights.accesslevelopenAccessfi
dc.subject.ysotietoturva
dc.subject.ysokyberturvallisuus
dc.subject.ysopääsynvalvonta
dc.subject.ysoverkkohyökkäykset
dc.subject.ysovalvontajärjestelmät
dc.subject.ysobig data
dc.subject.ysotiedonlouhinta
dc.subject.ysoalgoritmit
dc.subject.ysoklusterianalyysi


Aineistoon kuuluvat tiedostot

Thumbnail

Aineisto kuuluu seuraaviin kokoelmiin

Näytä suppeat kuvailutiedot