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

TL;DR

This study analyzes how different Android user profiles face varying malware risks, showing that tailored security approaches outperform generic solutions and highlighting the importance of user-specific risk assessment.

Contribution

It introduces a large-scale quantitative analysis of malware risk across user profiles, demonstrating the benefits of profile-based classification over traditional methods.

Findings

Profiles like gamers face over twice the malware risk.

Diversity of app signers correlates with higher risk.

Profile-based models improve malware detection accuracy.

Abstract

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