  Mobile malware has continued to grow at an alarming rate despite on-going
efforts towards mitigating the problem. This has been particularly noticeable
on Android due to its being an open platform that has subsequently overtaken
other platforms in the share of the mobile smart devices market. Hence,
incentivizing a new wave of emerging Android malware sophisticated enough to
evade most common detection methods. This paper proposes and investigates a
parallel machine learning based classification approach for early detection of
Android malware. Using real malware samples and benign applications, a
composite classification model is developed from parallel combination of
heterogeneous classifiers. The empirical evaluation of the model under
different combination schemes demonstrates its efficacy and potential to
improve detection accuracy. More importantly, by utilizing several classifiers
with diverse characteristics, their strengths can be harnessed not only for
enhanced Android malware detection but also quicker white box analysis by means
of the more interpretable constituent classifiers.
