An Android Malware Detection Approach to Enhance Node Feature Differences in a Function Call Graph Based on GCNs. [PDF]
Sensors (Basel), 2023 Wu H, Luktarhan N, Tian G, Song Y.
europepmc +1 more source
Android malware detection method based on highly distinguishable static features and DenseNet. [PDF]
PLoS One, 2022 Yang J, Zhang Z, Zhang H, Fan J.
europepmc +1 more source
On the evaluation of android malware detectors against code-obfuscation techniques. [PDF]
PeerJ Comput Sci, 2022 Nawaz U, Aleem M, Lin JC.
europepmc +1 more source
Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism. [PDF]
Sci Rep, 2023 Aurangzeb S, Aleem M.
europepmc +1 more source
Lightweight On-Device Detection of Android Malware Based on the Koodous Platform and Machine Learning. [PDF]
Sensors (Basel), 2022 Krzysztoń M, Bok B, Lew M, Sikora A.
europepmc +1 more source
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
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
FG-Droid: Grouping based feature size reduction for Android malware detection. [PDF]
PeerJ Comput Sci, 2022 Arslan RS.
europepmc +1 more source
Android Malware Detection Systems Review
Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 2017 With the smartphones entering our lives, the number of smartphones continues to increase day by day. The reason why smartphones are in so demand is that people can easily do what they want. According to IDC's 2016 Q2 report, Android dominated the smartphone market with an 87.6% share [1].
Ömer Kiraz, İbrahim Alper Doğru
openaire +1 more source