  We investigate the use of Android permissions as the vehicle to allow for
quick and effective differentiation between benign and malware apps. To this
end, we extract all Android permissions, eliminating those that have zero
impact, and apply two feature ranking algorithms namely Chi-Square test and
Fisher's Exact test to rank and additionally filter them, resulting in a
comparatively small set of relevant permissions. Then we use Decision Tree,
Support Vector Machine, and Random Forest Classifier algorithms to detect
malware apps. Our analysis indicates that this approach can result in better
accuracy and F-score value than other reported approaches. In particular, when
random forest is used as the classifier with the combination of Fisher's Exact
test, we achieve 99.34\% in accuracy and 92.17\% in F-score with the false
positive rate of 0.56\% for the dataset in question, with results improving to
99.82\% in accuracy and 95.28\% in F-score with the false positive rate as low
as 0.05\% when only malware from three most popular malware families are
considered.
