Android Malware Detection Using Machine Learning

dc.authorscopusid57220954646
dc.authorscopusid57220956711
dc.contributor.authorTahtaci, B.
dc.contributor.authorCanbay, B.
dc.date.accessioned2023-10-19T15:05:32Z
dc.date.available2023-10-19T15:05:32Z
dc.date.issued2020
dc.department-tempTahtaci, B., CRYPTTECH, Yapay Zeka Araştirma Laboratuvari, Istanbul, Turkey; Canbay, B., Kadir Has Üniversitesi, Bilgisayar Mühendisli?i Bölümü, Istanbul, Turkeyen_US
dc.description2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020 --15 October 2020 through 17 October 2020 -- --165305en_US
dc.description.abstractThe usage of mobile devices is increasing exponentially. There were lots of critical applications such as banking to health applications are available on mobile devices through mobile applications. This penetration and spread of mobile applications brings some threats. Malicious software(Malware) is one of these dangers. Malware has the potential to cause damage to various scales such as theft of sensitive data, identity and credit card. To reduce the effects of these threats, antiviruses have been developed and malware analysis teams have been established, but human effort may be insufficient in the rapidly growing malware market. For this reason, automated malware scanning solutions should be developed by making use of machine learning algorithms. In this study, machine learning models were created by using the n-gram features of the smali files, which are the decompiled Android packages. The trained models are combined with different feature extraction and feature selection methods and as a result their performances are reported. © 2020 IEEE.en_US
dc.identifier.citation15
dc.identifier.doi10.1109/ASYU50717.2020.9259834en_US
dc.identifier.isbn9781728191362
dc.identifier.scopus2-s2.0-85097952821en_US
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ASYU50717.2020.9259834
dc.identifier.urihttps://hdl.handle.net/20.500.12469/4938
dc.identifier.wosqualityN/A
dc.khas20231019-Scopusen_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Neworksen_US
dc.subjectMachine Learningen_US
dc.subjectMalware Detectionen_US
dc.subjectMobile Malwaresen_US
dc.subjectRandom Foresten_US
dc.subjectStatic Code Analysisen_US
dc.subjectAndroid (operating system)en_US
dc.subjectData privacyen_US
dc.subjectFeature extractionen_US
dc.subjectIntelligent systemsen_US
dc.subjectLearning algorithmsen_US
dc.subjectMachine learningen_US
dc.subjectMalwareen_US
dc.subjectMobile computingen_US
dc.subjectAndroid malwareen_US
dc.subjectCredit cardsen_US
dc.subjectCritical applicationsen_US
dc.subjectFeature selection methodsen_US
dc.subjectMachine learning modelsen_US
dc.subjectMalware analysisen_US
dc.subjectMobile applicationsen_US
dc.subjectSensitive datasen_US
dc.subjectMobile securityen_US
dc.titleAndroid Malware Detection Using Machine Learningen_US
dc.title.alternativeMakine Ö?renmesi Ile Android Zararli Yazilim Tespitien_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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