  With over 50 billion downloads and more than 1.3 million apps in the Google
official market, Android has continued to gain popularity amongst smartphone
users worldwide. At the same time there has been a rise in malware targeting
the platform, with more recent strains employing highly sophisticated detection
avoidance techniques. As traditional signature based methods become less potent
in detecting unknown malware, alternatives are needed for timely zero-day
discovery. Thus this paper proposes an approach that utilizes ensemble learning
for Android malware detection. It combines advantages of static analysis with
the efficiency and performance of ensemble machine learning to improve Android
malware detection accuracy. The machine learning models are built using a large
repository of malware samples and benign apps from a leading antivirus vendor.
Experimental results and analysis presented shows that the proposed method
which uses a large feature space to leverage the power of ensemble learning is
capable of 97.3 to 99 percent detection accuracy with very low false positive
rates.
