  Since Google unveiled Android OS for smartphones, malware are thriving with
3Vs, i.e. volume, velocity, and variety. A recent report indicates that one out
of every five business/industry mobile application leaks sensitive personal
data. Traditional signature/heuristic-based malware detection systems are
unable to cope up with current malware challenges and thus threaten the Android
ecosystem. Therefore recently researchers have started exploring machine
learning and deep learning based malware detection systems. In this paper, we
performed a comprehensive feature analysis to identify the significant Android
permissions and propose an efficient Android malware detection system using
machine learning and deep neural network. We constructed a set of $16$
permissions ($8\%$ of the total set) derived from variance threshold,
auto-encoders, and principal component analysis to build a malware detection
engine that consumes less train and test time without significant compromise on
the model accuracy. Our experimental results show that the Android malware
detection model based on the random forest classifier is most balanced and
achieves the highest area under curve score of $97.7\%$, which is better than
the current state-of-art systems. We also observed that deep neural networks
attain comparable accuracy to the baseline results but with a massive
computational penalty.
