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dc.contributor.authorJuvonen, Antti
dc.contributor.authorSipola, Tuomo
dc.contributor.authorHämäläinen, Timo
dc.date.accessioned2020-11-23T08:36:05Z
dc.date.available2020-11-23T08:36:05Z
dc.date.issued2015
dc.identifier.citationJuvonen, A., Sipola, T., & Hämäläinen, T. (2015). Online anomaly detection using dimensionality reduction techniques for HTTP log analysis. <i>Computer Networks</i>, <i>91</i>, 46-56. <a href="https://doi.org/10.1016/j.comnet.2015.07.019" target="_blank">https://doi.org/10.1016/j.comnet.2015.07.019</a>
dc.identifier.otherCONVID_24884430
dc.identifier.otherTUTKAID_67119
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/72722
dc.description.abstractModern web services face an increasing number of new threats. Logs are collected from almost all web servers, and for this reason analyzing them is beneficial when trying to prevent intrusions. Intrusive behavior often differs from the normal web traffic. This paper proposes a framework to find abnormal behavior from these logs. We compare random projection, principal component analysis and diffusion map for anomaly detection. In addition, the framework has online capabilities. The first two methods have intuitive extensions while diffusion map uses the Nyström extension. This fast out-of-sample extension enables real-time analysis of web server traffic. The framework is demonstrated using real-world network log data. Actual abnormalities are found from the dataset and the capabilities of the system are evaluated and discussed. These results are useful when designing next generation intrusion detection systems. The presented approach finds intrusions from high-dimensional datasets in real time.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier BV * North-Holland; International Council for Computer Communications
dc.relation.ispartofseriesComputer Networks
dc.rightsCC BY-NC-ND 4.0
dc.subject.otherAnomaly detection
dc.subject.otherDiffusion map
dc.subject.otherIntrusion detection
dc.subject.otherPrincipal component analysis
dc.subject.otherRandom projection
dc.titleOnline anomaly detection using dimensionality reduction techniques for HTTP log analysis
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202011236716
dc.contributor.laitosTietotekniikan laitosfi
dc.contributor.laitosDepartment of Mathematical Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2020-11-23T07:15:10Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange46–56
dc.relation.issn1389-1286
dc.relation.numberinseries0
dc.relation.volume91
dc.type.versionacceptedVersion
dc.rights.copyright© 2015 Elsevier
dc.rights.accesslevelopenAccessfi
dc.subject.ysokyberturvallisuus
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p26189
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.comnet.2015.07.019
dc.type.okmA1


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