A deep learning based android malware detection system with static analysis - DOĞAN, BUKET (My University, 2022)
© 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 %.
Keywords: Bilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği; Kontrol ve Sistem Mühendisliği; Sinyal İşleme; Bilgisayar Bilimleri; Algoritmalar; Yaşam Bilimleri; Temel Bilimler; Mühendislik ve Teknoloji; Information Systems, Communication and Control Engineering; Control and System Engineering; Signal Processing; Computer Sciences; algorithms; Life Sciences; Natural Sciences; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Yaşam Bilimleri (LIFE); Bilgisayar Bilimi; Mühendislik; Sinirbilim ve Davranış; OTOMASYON & KONTROL SİSTEMLERİ; BİLGİSAYAR BİLİMİ, YAPAY ZEKA; MÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK; Engineering, Computing & Technology (ENG); Life Sciences (LIFE); COMPUTER SCIENCE; ENGINEERING; NEUROSCIENCE & BEHAVIOR; AUTOMATION & CONTROL SYSTEMS; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; ENGINEERING, ELECTRICAL & ELECTRONIC; Yapay Zeka; Fizik Bilimleri; Bilgisayar Bilimi Uygulamaları; Bilgisayarla Görme ve Örüntü Tanıma; Kontrol ve Optimizasyon; İnsan Bilgisayar Etkileşimi; Artificial Intelligence; Physical Sciences; Computer Science Applications; Computer Vision and Pattern Recognition; Control and Optimization; Human-Computer Interaction; android system; deep learning; malware detection; RNN; static analysis