Diagnosing smartphone's abnormal behavior through robust outlier detection methods

Ali El Attar, Rida Khatoun, Marc Lemercier

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationGlobal Information Infrastructure Symposium, GIIS 2013
DOIs
Publication statusPublished - 1 Dec 2013
Externally publishedYes
Event2013 Global Information Infrastructure Symposium, GIIS 2013 - Trento, Italy
Duration: 28 Oct 201331 Oct 2013

Publication series

NameGlobal Information Infrastructure Symposium, GIIS 2013

Conference

Conference2013 Global Information Infrastructure Symposium, GIIS 2013
Country/TerritoryItaly
CityTrento
Period28/10/1331/10/13

Fingerprint

Dive into the research topics of 'Diagnosing smartphone's abnormal behavior through robust outlier detection methods'. Together they form a unique fingerprint.

Cite this