Smartphones contain information that is more sensitive and personal than
those found on computers and laptops. With an increase in the versatility of
smartphone functionality, more data has become vulnerable and exposed to
attackers. Successful mobile malware attacks could steal a user’s location,
photos, or even banking information. Due to a lack of post-attack strategies
firms also risk going out of business due to data theft. Thus, there is a need
besides just detecting malware intrusion in smartphones but to also identify
the data that has been stolen to assess, aid in recovery and prevent future
attacks. In this paper, we propose an accessible, non-intrusive machine
learning solution to not only detect malware intrusion but also identify the
type of data stolen for any app under supervision. We do this with Android
usage data obtained by utilising publicly available data collection framework-
SherLock. We test the performance of our architecture for multiple users on
real-world data collected using the same framework. Our architecture exhibits
less than 9% inaccuracy in detecting malware and can classify with 83%
certainty on the type of data that is being stolen.

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Author Of this post: <a href="http://arxiv.org/find/cs/1/au:+Venkatesh_S/0/1/0/all/0/1">Sai Vishwanath Venkatesh</a>, <a href="http://arxiv.org/find/cs/1/au:+Kumaran_P/0/1/0/all/0/1">Prasanna D. Kumaran</a>, <a href="http://arxiv.org/find/cs/1/au:+Bosco_J/0/1/0/all/0/1">Joish J Bosco</a>, <a href="http://arxiv.org/find/cs/1/au:+Kumaar_P/0/1/0/all/0/1">Pravin R. Kumaar</a>, <a href="http://arxiv.org/find/cs/1/au:+Vijayaraghavan_V/0/1/0/all/0/1">Vineeth Vijayaraghavan</a>

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