On the evaluation of android malware detectors against code-obfuscation techniques. [PDF]
PeerJ Comput Sci, 2022 Nawaz U, Aleem M, Lin JC.
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Androfim: few-shot android malware family detection based on image representation
CybersecurityAndroid malware is the major cyber threat to the popular Android platform which may influence millions of end users. To battle against the Android malware, a large number of machine learning methods either based on 1) traditional feature extraction using
Fan Zhou +4 more
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Android malware detection method based on highly distinguishable static features and DenseNet. [PDF]
PLoS One, 2022 Yang J, Zhang Z, Zhang H, Fan J.
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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.
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Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism. [PDF]
Sci Rep, 2023 Aurangzeb S, Aleem M.
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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.
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MFDroid: A Stacking Ensemble Learning Framework for Android Malware Detection. [PDF]
Sensors (Basel), 2022 Wang X, Zhang L, Zhao K, Ding X, Yu M.
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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.
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FG-Droid: Grouping based feature size reduction for Android malware detection. [PDF]
PeerJ Comput Sci, 2022 Arslan RS.
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HybFusion: A holistic Android malware detection framework with advanced feature fusion and ensemble learning. [PDF]
PLoS OneMinh Manh V, Do Xuan C, Van NTK.
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