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A deep learning based android malware detection system with static analysis

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dc.contributor.authorDOĞAN, BUKET
dc.contributor.authorsBayazit E. C. , Sahingoz O. K. , DOĞAN B.
dc.date.accessioned2022-12-26T11:13:38Z
dc.date.available2022-12-26T11:13:38Z
dc.date.issued2022-01-01
dc.description.abstract© 2022 IEEE.In recent years, smart mobile devices have become indispensable due to the availability of office applications, the Internet, game applications, vehicle guidance or similar most of our daily lives applications in addition to traditional services such as voice calls, SMSs, and multimedia services. Due to Android\"s open source structure and easy development platforms, the number of applications on Google Play, the official Android app store increased day by day. This also brig some security related issues for the end users. The increased popularity of Android operating system on mobile devices, and the associated financial benefits attracted attackers for developing some malware for these devices, which results a significant increase in the number of Android malware applications. To detect this type of security threats, signature based detection (static detection) in generally preferred due to its easy applicability and fast identification ability. Therefore in this study it is aimed to implement an up-to-date, effective, and reliable malware detection system with the help of some deep learning algorithms. In the proposed system, RNN-based LSTM, BiLSTM and GRU algorithms are evaluated on CICInvesAndMal2019 data set which contains 8115 static features for malware detection. Experimental results show that the BiLSTM model outperforms other proposed RNN-based deep learning methods with an accuracy rate of 98.85 %.
dc.identifier.citationBayazit E. C. , Sahingoz O. K. , DOĞAN B., \"A Deep Learning Based Android Malware Detection System with Static Analysis\", 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022, Ankara, Türkiye, 9 - 11 Haziran 2022
dc.identifier.doi10.1109/hora55278.2022.9800057
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85133954797&origin=inward
dc.identifier.urihttps://hdl.handle.net/11424/284020
dc.language.isoeng
dc.relation.ispartof4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectKontrol ve Sistem Mühendisliği
dc.subjectSinyal İşleme
dc.subjectBilgisayar Bilimleri
dc.subjectAlgoritmalar
dc.subjectYaşam Bilimleri
dc.subjectTemel Bilimler
dc.subjectMühendislik ve Teknoloji
dc.subjectInformation Systems, Communication and Control Engineering
dc.subjectControl and System Engineering
dc.subjectSignal Processing
dc.subjectComputer Sciences
dc.subjectalgorithms
dc.subjectLife Sciences
dc.subjectNatural Sciences
dc.subjectEngineering and Technology
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectYaşam Bilimleri (LIFE)
dc.subjectBilgisayar Bilimi
dc.subjectMühendislik
dc.subjectSinirbilim ve Davranış
dc.subjectOTOMASYON & KONTROL SİSTEMLERİ
dc.subjectBİLGİSAYAR BİLİMİ, YAPAY ZEKA
dc.subjectMÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK
dc.subjectEngineering, Computing & Technology (ENG)
dc.subjectLife Sciences (LIFE)
dc.subjectCOMPUTER SCIENCE
dc.subjectENGINEERING
dc.subjectNEUROSCIENCE & BEHAVIOR
dc.subjectAUTOMATION & CONTROL SYSTEMS
dc.subjectCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
dc.subjectENGINEERING, ELECTRICAL & ELECTRONIC
dc.subjectYapay Zeka
dc.subjectFizik Bilimleri
dc.subjectBilgisayar Bilimi Uygulamaları
dc.subjectBilgisayarla Görme ve Örüntü Tanıma
dc.subjectKontrol ve Optimizasyon
dc.subjectİnsan Bilgisayar Etkileşimi
dc.subjectArtificial Intelligence
dc.subjectPhysical Sciences
dc.subjectComputer Science Applications
dc.subjectComputer Vision and Pattern Recognition
dc.subjectControl and Optimization
dc.subjectHuman-Computer Interaction
dc.subjectandroid system
dc.subjectdeep learning
dc.subjectmalware detection
dc.subjectRNN
dc.subjectstatic analysis
dc.titleA deep learning based android malware detection system with static analysis
dc.typeconferenceObject
dspace.entity.typePublication
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local.indexed.atSCOPUS
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