Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6248
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTerzi, Ramazan-
dc.contributor.authorYavanoğlu, Uraz-
dc.contributor.authorSinanç, Duygu-
dc.contributor.authorOğuz, Doğaç-
dc.contributor.authorÇakır, Semra-
dc.date.accessioned2021-09-11T15:35:28Z-
dc.date.available2021-09-11T15:35:28Z-
dc.date.issued2014en_US
dc.identifier.citation13th International Conference on Machine Learning and Applications (ICMLA) -- DEC 03-06, 2014 -- Detroit, MIen_US
dc.identifier.isbn978-1-4799-7415-3-
dc.identifier.urihttps://doi.org/10.1109/ICMLA.2014.82-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6248-
dc.description.abstractIn this study, malicious users who cause to resource exhausting are tried to detect in a telecommunication company network. Non-Legitimate users could cause lack of information availability and need countermeasures to prevent threat or limit permissions on the system. For this purpose, ANN based intelligent system is proposed and compared to SVM which is well known classification technique. According to results, proposed technique has achieved approximately 70% general success rate, 33% false positive rate and 27% false negative rate in controlled environment. Also ANN has high ability to work compare to SVM for our dataset. As a result proposed technique and developed application shows sufficient and acceptable defense mechanism in huge company networks. We discussed about this is initial study and ongoing research which is compared to the current literature. By the way, this study also shows that non security information such as users mobile experiences could be potential usage to prevent resource exhausting also known as DoS related attacks.en_US
dc.description.sponsorshipIEEE Comp Soc, AML&A, IEEE, Wayne State Univen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2014 13Th International Conference On Machine Learning And Applications (Icmla)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectresource exhaustingen_US
dc.subjectartificial neural network(ANN)en_US
dc.subjectDoS attacken_US
dc.subjectmobile store securityen_US
dc.titleAn Intelligent Technique for Detecting Malicious Users on Mobile Storesen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage470en_US
dc.identifier.endpage477en_US
dc.authorid0000-0002-3332-9414-
dc.authorid0000-0003-2345-8666-
dc.identifier.wosWOS:000380459000079en_US
dc.identifier.scopus2-s2.0-84946692877en_US
dc.institutionauthorOğuz, Doğaç-
dc.identifier.doi10.1109/ICMLA.2014.82-
dc.relation.publicationcategoryDiğeren_US
dc.relation.conference13th International Conference on Machine Learning and Applications (ICMLA)en_US
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeConference Object-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

Page view(s)

48
checked on Nov 11, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.