BlockDroid: detection of Android malware from images using lightweight convolutional neural network models with ensemble learning and blockchain for mobile devices. [PDF]
PeerJ Comput SciŞafak E +3 more
europepmc +1 more source
Z2F: Heterogeneous graph-based Android malware detection. [PDF]
PLoS OneMa Z, Luktarhan N.
europepmc +1 more source
Deep Feature Extraction and Classification of Android Malware Images. [PDF]
Sensors (Basel), 2020 Singh J +4 more
europepmc +1 more source
Novel Multi-Classification Dynamic Detection Model for Android Malware Based on Improved Zebra Optimization Algorithm and LightGBM. [PDF]
Sensors (Basel)Zhou S, Li H, Fu X, Han D, He X.
europepmc +1 more source
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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Chat-GPT for Android malware detection
The use of large-language models (LLMs) in the field of cybersecurity has been increasing greatly in recent years. With the advent of ChatGPT by OpenAI, there have been many different use cases for LLMs in cybersecurity, including in intrusion detection,
Ong, Eliezer De Zhi
core
Adversarial Samples on Android Malware Detection Systems for IoT Systems. [PDF]
Sensors (Basel), 2019 Liu X +5 more
europepmc +1 more source
Detecting and classifying method based on similarity matching of Android malware behavior with profile. [PDF]
Springerplus, 2016 Jang JW +4 more
europepmc +1 more source
OmBNNet: a resource-efficient FPGA-based obfuscated malware detection method using binarized neural network. [PDF]
Sci RepDas K +3 more
europepmc +1 more source