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dc.contributor.authorDo, Van Thuan
dc.contributor.authorEngelstad, Paal E.
dc.contributor.authorFeng, Boning
dc.contributor.authorDo, van Thanh
dc.date.accessioned2018-03-09T14:40:30Z
dc.date.accessioned2018-06-15T13:19:05Z
dc.date.available2018-03-09T14:40:30Z
dc.date.available2018-06-15T13:19:05Z
dc.date.issued2017
dc.identifier.citationDo VT, Engelstad P.E., Feng B, Do VTDO. Detection of DNS tunneling in mobile networks using machine learning. Lecture Notes in Electrical Engineering. 2017;424:221-230en
dc.identifier.isbn978-981-10-4153-2
dc.identifier.issn1876-1100
dc.identifier.urihttps://hdl.handle.net/10642/5962
dc.description.abstractLately, costly and threatening DNS tunnels on the mobile networks bypassing the mobile operator’s Policy and Charging Enforcement Function (PCEF), has shown the vulnerability of the mobile networks caused by the Domain Name System (DNS) which calls for protection solutions. Unfortunately there is currently no really adequate solution. This paper proposes to use machine learning techniques in the detection and mitigation of a DNS tunneling in mobile networks. Two machine learning techniques, namely One Class Support Vector Machine (OCSVM) and K-Means are experimented and the results prove that machine learning techniques could yield quite efficient detection solutions. The paper starts with a comprehensive introduction to DNS tunneling in mobile networks. Next the challenges in DNS tunneling detections are reviewed. The main part of the paper is the description of proposed DNS tunneling detection using machine learning.en
dc.language.isoenen
dc.publisherSpringer Verlagen
dc.relation.ispartofseriesLecture Notes in Electrical Engineering;Information Science and Applications 2017, Volume 424
dc.subjectMobile network securityen
dc.subjectMachine learningen
dc.subjectMobile vulnerabilityen
dc.subjectCyber securityen
dc.titleDetection of DNS tunneling in mobile networks using machine learningen
dc.typeJournal articleen
dc.typePeer revieweden
dc.date.updated2018-03-09T14:40:30Z
dc.description.versionsubmittedVersionen
dc.identifier.doihttp://dx.doi.org/10.1007/978-981-10-4154-9_26
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dc.source.issn1876-1100
dc.source.issn1876-1119
dc.relation.journalLecture Notes in Electrical Engineering


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