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Malware detection on mobile devices using distributed machine learning

 
: Sharifi Shamili, A.; Bauckhage, C.; Alpcan, T.

:

International Association for Pattern Recognition -IAPR-; Institute of Electrical and Electronics Engineers -IEEE-:
ICPR 2010, 20th International Conference on Pattern Recognition. Proceedings : 23-26 August, 2010, Istanbul, Turkey
Piscataway, NJ: IEEE, 2010
ISBN: 978-0-7695-4109-9
ISBN: 978-1-4244-7542-1
ISBN: 1-4244-7542-2
S.4348-4351
International Conference on Pattern Recognition (ICPR) <20, 2010, Istanbul>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

Abstract
This paper presents a distributed Support Vector Machine (SVM) algorithm in order to detect malicious software (malware) on a network of mobile devices. The light-weight system monitors mobile user activity in a distributed and privacy-preserving way using a statistical classification model which is evolved by training with examples of both normal usage patterns and unusual behavior. The system is evaluated using the MIT reality mining data set. The results indicate that the distributed learning system trains quickly and performs reliably. Moreover, it is robust against failures of individual components.

: http://publica.fraunhofer.de/dokumente/N-147661.html