ID:24287
Type of Publication: Journal Articles
Authors: Bracha Shapira,
Title: Detecting application update attack on mobile devices through network features
Name of the Journal: 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
Year: 2013
Pages: 91 - 2
Abstract: Recently, a new type of mobile malware applications with self-updating capabilities was found on the official Google Android marketplace. Malware applications of this type cannot be detected using the standard signatures approach or by applying regular static or dynamic analysis methods. In this paper we first describe and analyze this new type of mobile malware and then present a new network-based behavioral analysis for identifying such malware applications. For each application, a model representing its specific traffic pattern is learned locally on the device. Machine-learning methods are used for learning the normal patterns and detection of deviations from the normal application's behavior. These methods were implemented and evaluated on Android devices.
Keywords: invasive software;learning (artificial intelligence);mobile computing;smart phones;telecommunication security;telecommunication traffic; ,
Url: http://dx.doi.org/10.1109/INFCOMW.2013.6970755
Last Updated: 1/13/2016 12:00:00 AM
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