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Deep learning based malware detection for android systems: A comparative analysis

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dc.contributor.authorDOĞAN, BUKET
dc.contributor.authorsBayazit E. C., Sahingoz O. K., DOĞAN B.
dc.date.accessioned2023-05-22T08:11:53Z
dc.date.available2023-05-22T08:11:53Z
dc.date.issued2023-04-01
dc.description.abstractNowadays, cyber attackers focus on Android, which is the most popular open-source operating system, as main target by applying some malicious software (malware) to access users\" private information, control the device, or harm end-users. To detect Android malware, security experts have offered some learning-based models. In this study, we developed an Android malware detection system that uses different machine\deep learning models by performing both dynamic analyses, in which suspected malware is executed in a safe environment for observing its behaviours, and static analysis, which examines a malware file without any execution on the Android device. The benefits and weaknesses of these models and analyses are described in detail in this comparative study, and directions for future studies are drawn. Experimental results showed that the proposed models gave better results than those in the literature, with 0.988 accuracy for LSTM on static analysis and 0.953 accuracy for CNN-LSTM on dynamic analysis.
dc.identifier.citationBayazit E. C., Sahingoz O. K., DOĞAN B., "Deep Learning based Malware Detection for Android Systems: A Comparative Analysis", TEHNICKI VJESNIK-TECHNICAL GAZETTE, cilt.30, sa.3, ss.787-796, 2023
dc.identifier.doi10.17559/tv-20220907113227
dc.identifier.endpage796
dc.identifier.issn1330-3651
dc.identifier.issue3
dc.identifier.startpage787
dc.identifier.urihttps://avesis.marmara.edu.tr/api/publication/22753d3f-71c6-448e-a0d6-11f1dabb338a/file
dc.identifier.urihttps://hdl.handle.net/11424/289485
dc.identifier.volume30
dc.language.isoeng
dc.relation.ispartofTEHNICKI VJESNIK-TECHNICAL GAZETTE
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectHarita Mühendisliği-Geomatik
dc.subjectMühendislik ve Teknoloji
dc.subjectGeotechnical Engineering
dc.subjectEngineering and Technology
dc.subjectMÜHENDİSLİK, ÇOK DİSİPLİNLİ
dc.subjectMühendislik
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectENGINEERING, MULTIDISCIPLINARY
dc.subjectENGINEERING
dc.subjectEngineering, Computing & Technology (ENG)
dc.subjectGenel Mühendislik
dc.subjectMedya Teknolojisi
dc.subjectMühendislik (çeşitli)
dc.subjectFizik Bilimleri
dc.subjectGeneral Engineering
dc.subjectMedia Technology
dc.subjectEngineering (miscellaneous)
dc.subjectPhysical Sciences
dc.subjectandroid
dc.subjectdeep learning
dc.subjectmalware detection systems
dc.subjectmalware analysis
dc.subjectMODEL
dc.subjectandroid
dc.subjectdeep learning
dc.subjectmalware detection systems
dc.subjectmalware analysis
dc.titleDeep learning based malware detection for android systems: A comparative analysis
dc.typearticle
dspace.entity.typePublication
local.avesis.id22753d3f-71c6-448e-a0d6-11f1dabb338a
local.indexed.atWOS
relation.isAuthorOfPublicationa9bc5daa-70d8-426a-a198-3e5753ae7958
relation.isAuthorOfPublication.latestForDiscoverya9bc5daa-70d8-426a-a198-3e5753ae7958

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