@inproceedings{attar_diagnosing_2013,
	title = {Diagnosing smartphone's abnormal behavior through robust outlier detection methods},
	doi = {10.1109/GIIS.2013.6684358},
	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.},
	booktitle = {Global {Information} {Infrastructure} {Symposium} - {GIIS} 2013},
	author = {Attar, A. E. and Khatoun, R. and Lemercier, M.},
	month = oct,
	year = {2013},
	keywords = {3G networks, Batteries, Covariance matrices, Ellipsoids, MCD, MVE, Malware, Robustness, Smart phones, business environment, covariance analysis, minimum covariance determinant, minimum volume ellipsoid, robust outlier detection methods, smart phone abnormal behavior diagnosis, smart phones, statistical methods},
	pages = {1--3}
}

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