  Mobile malware has been growing in scale and complexity as smartphone usage
continues to rise. Android has surpassed other mobile platforms as the most
popular whilst also witnessing a dramatic increase in malware targeting the
platform. A worrying trend that is emerging is the increasing sophistication of
Android malware to evade detection by traditional signature-based scanners. As
such, Android app marketplaces remain at risk of hosting malicious apps that
could evade detection before being downloaded by unsuspecting users. Hence, in
this paper we present an effective approach to alleviate this problem based on
Bayesian classification models obtained from static code analysis. The models
are built from a collection of code and app characteristics that provide
indicators of potential malicious activities. The models are evaluated with
real malware samples in the wild and results of experiments are presented to
demonstrate the effectiveness of the proposed approach.
