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 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.