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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
PubMed
Summary
This summary is machine-generated.

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.

Failed At:

2026-06-19T13:39:48.718063+00:00

Keywords:
Convolutional neural networkFuzzy logicMalware detectionMobile securityPermission

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