MFDroid: A Stacking Ensemble Learning Framework for Android Malware Detection. [PDF]
Sensors (Basel), 2022 Wang X, Zhang L, Zhao K, Ding X, Yu M.
europepmc +1 more source
Unmasking the Veiled: A Comprehensive Analysis of Android Evasive Malware
International audienceSince Android is the most widespread operating system, malware targeting it poses a severe threat to the security and privacy of millions of users and is increasing from year to year.
Balzarotti, Davide +5 more
core +1 more source
Convolution neural network with batch normalization and inception-residual modules for Android malware classification. [PDF]
Sci Rep, 2022 Liu T, Zhang H, Long H, Shi J, Yao Y.
europepmc +1 more source
Advanced Android Malware Analysis
, 2015 With the ever increasing development of software, mobile malware has also become increasingly sophisticated. Obfuscation is now becoming obsolete and being replaced by run-time packers in which mobile malware is protected.
Éliás, Tibor
core +1 more source
YATSIDroid: an android malware detection framework based on artificial immune system. [PDF]
Sci RepMahindru A +6 more
europepmc +1 more source
FG-Droid: Grouping based feature size reduction for Android malware detection. [PDF]
PeerJ Comput Sci, 2022 Arslan RS.
europepmc +1 more source
Malware Detection Techniques in Android
, 2015 Mobile Phones have become an important need of today. The term mobile phone and smart phone are almost identical now-a-days. Smartphone market is booming with very high speed.
Amit Jain, Pallavi Kaushik
core
HybFusion: A holistic Android malware detection framework with advanced feature fusion and ensemble learning. [PDF]
PLoS OneMinh Manh V, Do Xuan C, Van NTK.
europepmc +1 more source
Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers. [PDF]
PeerJ Comput Sci, 2022 Atacak İ, Kılıç K, Doğru İA.
europepmc +1 more source
Reassessing feature-based Android malware detection in a contemporary context. [PDF]
PLoS OneMuzaffar A +3 more
europepmc +1 more source