Diagnosing smartphone's abnormal behavior through robust outlier detection methods
| Autor: | Ali El Attar, Marc Lemercier, Rida Khatoun |
|---|---|
| Přispěvatelé: | Environnement de Réseaux Autonomes (ERA), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS) |
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Analysis of covariance
Computer science Volume (computing) 020207 software engineering 02 engineering and technology Covariance computer.software_genre Ellipsoid Business environment [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] [INFO.INFO-MC]Computer Science [cs]/Mobile Computing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Anomaly detection Data mining Abnormality computer ComputingMilieux_MISCELLANEOUS |
| Zdroj: | 2013 Global Information Infrastructure Symposium 2013 Global Information Infrastructure Symposium, Oct 2013, Trento, Italy. pp.1-3 GIIS |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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