  Smartphones have become an intrinsic part of human's life. The smartphone
unifies diverse advanced characteristics. It enables users to store various
data such as photos, health data, credential bank data, and personal
information. The Android operating system is the prevalent mobile operating
system and, in the meantime, the most targeted operating system by malware
developers. Recently the unparalleled development of Android malware put
pressure on researchers to propose effective methods to suppress the spread of
the malware. In this paper, we propose a deep learning approach for Android
malware detection. The proposed approach investigates five different feature
sets and applies Autoencoder to identify malware. The experimental results show
that the proposed approach can identify malware with high accuracy.
