Deep Learning for Android Malware Defenses: A Systematic Literature Review
ACM Computing Surveys, 2023Yue Liu +2 more
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ML for Android Malware Detection
The past few years have witnessed the drastic increase of mobile apps providing various facilities for personal andbusiness use. The proliferation of mobile apps is due to billions of users who enable developers to earn revenue throughadvertisements, in-app purchases, etc. Whenever users install a new app, they are under the risk of installing malware.openaire +1 more source
A survey of malware detection in Android apps: Recommendations and perspectives for future research
Computer Science Review, 2021Raphaël Khoury +2 more
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EfficientNet convolutional neural networks-based Android malware detection
Computers and Security, 2022Pooja Yadav +2 more
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GDroid: Android malware detection and classification with graph convolutional network
Computers and Security, 2021Shaoyin Cheng, Weiming Zhang
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Android malware concept drift using system calls: Detection, characterization and challenges
Expert Systems With Applications, 2022Marcin Luckner, Hayretdin Bahsi
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DL-Droid: Deep learning based android malware detection using real devices
Computers and Security, 2020Suleiman Yerima, Sakir Sezer
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Android malware obfuscation variants detection method based on multi-granularity opcode features
Future Generation Computer Systems, 2022Ruixuan Li, Yu Jiang
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Android Security: A Survey of Issues, Malware Penetration, and Defenses
IEEE Communications Surveys and Tutorials, 2015Vijay Laxmi, Parvez Farui, Mauro Conti
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Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
IEEE Transactions on Information Forensics and Security, 2020Xiao Chen, Chaoran Li, Derui Wang
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