  With the increasing popularity of Android in the last decade, Android is
popular among users as well as attackers. The vast number of android users
grabs the attention of attackers on android. Due to the continuous evolution of
the variety and attacking techniques of android malware, our detection methods
should need an update too. Most of the researcher's works are based on static
features, and very few focus on dynamic features. In this paper, we are filling
the literature gap by detecting android malware using System calls. We are
running the malicious app in a monitored and controlled environment using an
emulator to detect malware. Malicious behavior is activated with some simulated
events during its runtime to activate its hostile behavior. Logs collected
during the app's runtime are analyzed and fed to different machine learning
models for Detection and Family classification of Malware. The result indicates
that K-Nearest Neighbor and the Decision Tree gave the highest accuracy in
malware detection and Family Classification respectively.
