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Malicious applications pose a threat to the security of the Android platform. The growing amount and diversity of these applications render conventional defenses largely ineffective and thus Android smartphones often remain un-protected from novel malware. In this paper, we propose DREBIN, a lightweight method for detection of Android malware that enables identifying malicious applications di-rectly on the smartphone. As the limited resources impede monitoring applications at run-time, DREBIN performs a broad static analysis, gathering as many features of an ap-plication as possible. These features are embedded in a joint vector space, such that typical patterns indicative for malware can be automatically identified and used for ex-plaining the decisions of our method. In an evaluation with 123,453 applications and 5,560 malware samples DREBIN outperforms several related approaches and detects 94% of the malware with few false alarms, where the explana-tions provided for each detection reveal relevant properties of the detected malware. On five popular smartphones, the method requires 10 seconds for an analysis on average, ren-dering it suitable for checking downloaded applications di-rectly on the device. 1
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Daniel J. Arp
Michael Spreitzenbarth
Michael Huebner
University of Göttingen
Friedrich-Alexander-Universität Erlangen-Nürnberg
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Arp et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69d8491cd56ca42147d18278 — DOI: https://doi.org/10.14722/ndss.2014.23247
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