Z2F: Heterogeneous graph-based Android malware detection. [PDF]
PLoS OneAndroid malware is becoming more common, and its invasion of smart devices has brought immeasurable losses to people’s lives. Most existing Android malware detection methods extract Android features from the original application files without considering the high-order hidden information behind them, but these hidden information can reflect malicious ...
Ma Z, Luktarhan N.
europepmc +4 more sources
BERT ensemble based MBR framework for android malware detection. [PDF]
Sci RepAlsubaei FS +5 more
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Adversarial Samples on Android Malware Detection Systems for IoT Systems. [PDF]
Sensors (Basel), 2019 Liu X +5 more
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Hybrid deep learning model for accurate and efficient android malware detection using DBN-GRU. [PDF]
PLoS OneKauser Sk H, Anu V M.
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Machine learning models and dimensionality reduction for improving the Android malware detection. [PDF]
PeerJ Comput SciMorán P +4 more
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Process control block information dataset: Towards android malware detection. [PDF]
Data BriefAlawneh H, Alkofahi H.
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Deep learning-based improved transformer model on android malware detection and classification in internet of vehicles. [PDF]
Sci RepAlmakayeel N.
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PermQRDroid: Android malware detection with novel attention layered mini-ResNet architecture over effective permission information image. [PDF]
PeerJ Comput SciKılıç K, Doğru İA, Toklu S.
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PermDroid a framework developed using proposed feature selection approach and machine learning techniques for Android malware detection. [PDF]
Sci RepMahindru A +6 more
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