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
Ma Z, Luktarhan N.
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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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OmBNNet: a resource-efficient FPGA-based obfuscated malware detection method using binarized neural network. [PDF]
Sci RepDas K +3 more
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Efficient feature ranked hybrid framework for android Iot malware detection. [PDF]
Sci RepSaeed NH +3 more
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Few-shot android malware classification with quantum-enhanced prototypical learning and drift detection. [PDF]
Sci RepTawfik M +5 more
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Improving malware detection performance using hybrid deep representation learning with heuristic search algorithms. [PDF]
Sci RepAnuradha A, Chouhan AS, Srinivas Rao S.
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DEFEAT: Android device behavior-based datasets for multi-stage APT. [PDF]
Data BriefJabar T, Al-Kadhimi AA, Singh MM.
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MH-1M: A 1.34 Million-Sample Multi-Feature Android Malware Dataset with Rich Metadata. [PDF]
Sci DataBragança H +4 more
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