DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android


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.

DOI: 10.1007/978-3-319-04283-1_6

Extracted Key Phrases

9 Figures and Tables

Citations per Year

184 Citations

Semantic Scholar estimates that this publication has 184 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Aafer2013DroidAPIMinerMA, title={DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android}, author={Yousra Aafer and Wenliang Du and Heng Yin}, booktitle={SecureComm}, year={2013} }