Research Article
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android
@INPROCEEDINGS{10.1007/978-3-319-04283-1_6, author={Yousra Aafer and Wenliang Du and Heng Yin}, title={DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android}, proceedings={Security and Privacy in Communication Networks. 9th International ICST Conference, SecureComm 2013, Sydney, NSW, Australia, September 25-28, 2013, Revised Selected Papers}, proceedings_a={SECURECOMM}, year={2014}, month={6}, keywords={Android malware static detection classification}, doi={10.1007/978-3-319-04283-1_6} }- Yousra Aafer
Wenliang Du
Heng Yin
Year: 2014
DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android
SECURECOMM
Springer
DOI: 10.1007/978-3-319-04283-1_6
Abstract
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 and 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.