  Mobile malware has been growing in scale and complexity spurred by the
unabated uptake of smartphones worldwide. Android is fast becoming the most
popular mobile platform resulting in sharp increase in malware targeting the
platform. Additionally, Android malware is evolving rapidly to evade detection
by traditional signature-based scanning. Despite current detection measures in
place, timely discovery of new malware is still a critical issue. This calls
for novel approaches to mitigate the growing threat of zero-day Android
malware. Hence, in this paper we develop and analyze proactive Machine Learning
approaches based on Bayesian classification aimed at uncovering unknown Android
malware via static analysis. The study, which is based on a large malware
sample set of majority of the existing families, demonstrates detection
capabilities with high accuracy. Empirical results and comparative analysis are
presented offering useful insight towards development of effective
static-analytic Bayesian classification based solutions for detecting unknown
Android malware.
