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Previous work has investigated the particularities of security practiceswithin specific user communities defined based on country of origin, age, priortech abuse, and economic status. Their results highlight that current securitysolutions that adopt a one-size-fits-all-users approach ignore the differencesand needs of particular user communities. However, those works focus on asingle community or cluster users into hard-to-interpret sub-populations.  In this work, we perform a large-scale quantitative analysis of the risk ofencountering malware and other potentially unwanted applications (PUA) acrossuser communities. At the core of our study is a dataset of app installationlogs collected from 12M Android mobile devices. Leveraging user-installed apps,we define intuitive profiles based on users' interests (e.g., gamers andinvestors), and fit a subset of 5.4M devices to those profiles. Our analysis isstructured in three parts. First, we perform risk analysis on the wholepopulation to measure how the risk of malicious app encounters is affected bydifferent factors. Next, we create different profiles to investigate whetherrisk differences across users may be due to their interests. Finally, wecompare a per-profile approach for classifying clean and infected devices withthe classical approach that considers the whole population.  We observe that features such as the diversity of the app signers and the useof alternative markets highly correlate with the risk of malicious appencounters. We also discover that some profiles such as gamers and social-mediausers are exposed to more than twice the risks experienced by the averageusers. We also show that the classification outcome has a marked accuracyimprovement when using a per-profile approach to train the prediction models.Overall, our results confirm the inadequacy of one-size-fits-all protectionsolutions.

One Size Does not Fit All: Quantifying the Risk of Malicious App Encounters for Different Android User Profiles