DocumentCode :
654154
Title :
Diagnosing smartphone´s abnormal behavior through robust outlier detection methods
Author :
El Attar, Ali ; Khatoun, Rida ; Lemercier, Marc
Author_Institution :
ICD / ERA &STMR, Univ. of Technol. of Troyes (UTT), Troyes, France
fYear :
2013
fDate :
28-31 Oct. 2013
Firstpage :
1
Lastpage :
3
Abstract :
Smartphones have become increasingly popular and nowadays with the using of 3G networks, the needs in terms of connectivity in a business environment are substantial. Malicious use of such devices is highly dangerous since users may be victims of such use. In this paper, we present two statistical methods (Minimum Covariance Determinant (MCD) and Minimum Volume Ellipsoid (MVE) used to detect abnormal smartphone´s applications. Initial experiments results prove the efficiency and the accuracy of the MVE and MCD in detecting abnormal smartphone´s applications.
Keywords :
covariance analysis; smart phones; 3G networks; MCD; MVE; business environment; minimum covariance determinant; minimum volume ellipsoid; robust outlier detection methods; smart phone abnormal behavior diagnosis; statistical methods; Batteries; Covariance matrices; Ellipsoids; Malware; Robustness; Smart phones;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Global Information Infrastructure Symposium, 2013
Conference_Location :
Trento
Type :
conf
DOI :
10.1109/GIIS.2013.6684358
Filename :
6684358
Link To Document :
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