Reference : Large-scale Machine Learning-based Malware Detection: Confronting the "10-fold Cross ... |
Scientific congresses, symposiums and conference proceedings : Paper published in a book | |||
Engineering, computing & technology : Computer science | |||
http://hdl.handle.net/10993/18024 | |||
Large-scale Machine Learning-based Malware Detection: Confronting the "10-fold Cross Validation" Scheme with Reality | |
English | |
Allix, Kevin ![]() | |
Bissyande, Tegawendé François D Assise ![]() | |
Jerome, Quentin ![]() | |
Klein, Jacques ![]() | |
State, Radu ![]() | |
Le Traon, Yves ![]() | |
Mar-2014 | |
Proceedings of the 4th ACM Conference on Data and Application Security and Privacy | |
ACM | |
CODASPY '14 | |
163--166 | |
Yes | |
978-1-4503-2278-2 | |
New York, NY, USA | |
4th ACM Conference on Data and Application Security and Privacy | |
from 03-03-2014 to 05-03-2014 | |
San Antonio, Texas | |
USA | |
[en] android ; machine learning ; malware ; ten-fold | |
[en] To address the issue of malware detection, researchers have
recently started to investigate the capabilities of machine- learning techniques for proposing effective approaches. Sev- eral promising results were recorded in the literature, many approaches being assessed with the common “10-Fold cross validation” scheme. This paper revisits the purpose of mal- ware detection to discuss the adequacy of the “10-Fold” scheme for validating techniques that may not perform well in real- ity. To this end, we have devised several Machine Learning classifiers that rely on a novel set of features built from ap- plications’ CFGs. We use a sizeable dataset of over 50,000 Android applications collected from sources where state-of- the art approaches have selected their data. We show that our approach outperforms existing machine learning-based approaches. However, this high performance on usual-size datasets does not translate in high performance in the wild. | |
http://hdl.handle.net/10993/18024 | |
10.1145/2557547.2557587 | |
http://doi.acm.org/10.1145/2557547.2557587 |
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