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Android malware detection using hybrid ANFIS architecture with low computational cost convolutional layers
İsmail Atacak1, Kazım Kılıç1, İbrahim Alper Doğru1
1IoTLab, Department of Computer Engineering, Faculty of Technology, Gazi University, Ankara, Turkey.
Peerj. Computer Science
|October 20, 2022
Summary
This study introduces a hybrid architecture for Android malware detection using convolutional neural networks (CNNs) and fuzzy logic. The novel approach effectively identifies malicious applications based on permission information, achieving high accuracy.
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2026-06-19T13:39:48.718063+00:00
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