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Mobile malware exposed

Authors: Alaa Salman; Imad H. Elhajj; Ali Chehab; Ayman I. Kayssi;

Mobile malware exposed

Abstract

In this paper, we propose a new method to detect malicious activities on mobile devices by examining an application's runtime behavior. To this end, we use the Xposed framework to build a monitoring module that generates behavior profiles for applications. The module integrates with our intrusion detection system which then analyzes and reports on the profiles. We use this tool to detect malicious behavior patterns using both a custom-written malware and a real one. We also detect behavior patterns for some popular applications from the Google Play Store to expose their functionality. The results show that standard techniques that are used to evade static analysis are not effective against our monitoring approach. This approach can also be generalized to detect unknown malware or expose exact application behavior to the user.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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