  Android malware has been on the rise in recent years due to the increasing
popularity of Android and the proliferation of third party application markets.
Emerging Android malware families are increasingly adopting sophisticated
detection avoidance techniques and this calls for more effective approaches for
Android malware detection. Hence, in this paper we present and evaluate an
n-gram opcode features based approach that utilizes machine learning to
identify and categorize Android malware. This approach enables automated
feature discovery without relying on prior expert or domain knowledge for
pre-determined features. Furthermore, by using a data segmentation technique
for feature selection, our analysis is able to scale up to 10-gram opcodes. Our
experiments on a dataset of 2520 samples showed an f-measure of 98% using the
n-gram opcode based approach. We also provide empirical findings that
illustrate factors that have probable impact on the overall n-gram opcodes
performance trends.
