  Today anti-malware community is facing challenges due to the ever-increasing
sophistication and volume of malware attacks developed by adversaries.
Traditional malware detection mechanisms are not able to cope-up with
next-generation malware attacks. Therefore in this paper, we propose effective
and efficient Android malware detection models based on machine learning and
deep learning integrated with clustering. We performed a comprehensive study of
different feature reduction, classification and clustering algorithms over
various performance metrics to construct the Android malware detection models.
Our experimental results show that malware detection models developed using
Random Forest eclipsed deep neural network and other classifiers on the
majority of performance metrics. The baseline Random Forest model without any
feature reduction achieved the highest AUC of 99.4%. Also, the segregating of
vector space using clustering integrated with Random Forest further boosted the
AUC to 99.6% in one cluster and direct detection of Android malware in another
cluster, thus reducing the curse of dimensionality. Additionally, we found that
feature reduction in detection models does improve the model efficiency
(training and testing time) many folds without much penalty on the
effectiveness of the detection model.
