  Android malware is one of the most dangerous threats on the internet, and
it's been on the rise for several years. Despite significant efforts in
detecting and classifying android malware from innocuous android applications,
there is still a long way to go. As a result, there is a need to provide a
basic understanding of the behavior displayed by the most common Android
malware categories and families. Each Android malware family and category has a
distinct objective. As a result, it has impacted every corporate area,
including healthcare, banking, transportation, government, and e-commerce. In
this paper, we presented two machine-learning approaches for Dynamic Analysis
of Android Malware: one for detecting and identifying Android Malware
Categories and the other for detecting and identifying Android Malware
Families, which was accomplished by analyzing a massive malware dataset with 14
prominent malware categories and 180 prominent malware families of
CCCS-CIC-AndMal2020 dataset on Dynamic Layers. Our approach achieves in Android
Malware Category detection more than 96 % accurate and achieves in Android
Malware Family detection more than 99% accurate. Our approach provides a method
for high-accuracy Dynamic Analysis of Android Malware while also shortening the
time required to analyze smartphone malware.
