2013 | OriginalPaper | Chapter
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android
Authors : Yousra Aafer, Wenliang Du, Heng Yin
Published in: Security and Privacy in Communication Networks
Publisher: Springer International Publishing
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The increasing popularity of Android apps makes them the target of malware authors. To defend against this severe increase of Android malwares and help users make a better evaluation of apps at install time, several approaches have been proposed. However, most of these solutions suffer from some shortcomings; computationally expensive, not general or not robust enough. In this paper, we aim to mitigate Android malware installation through providing
robust
and
lightweight
classifiers. We have conducted a thorough analysis to extract relevant features to malware behavior captured at API level, and evaluated different classifiers using the generated feature set. Our results show that we are able to achieve an accuracy as high as 99% and a false positive rate as low as 2.2% using KNN classifier.