Understanding the Sneaky Patterns of Pop-up Windows in the Mobile Ecosystem
Dongpeng Wu, Yuhong Nan, Shaojiang Wang, Jiawei Wang, Luwa Li, Xueqiang Wang

TL;DR
This paper identifies and analyzes sneaky patterns of pop-up windows in mobile apps that manipulate users and degrade experience, introducing an automated detection tool and revealing regional differences in their deployment.
Contribution
It introduces five distinct sneaky patterns in mobile pop-up windows and develops Poker, an automated pipeline for detecting and analyzing these patterns in real-world apps.
Findings
Poker achieves high precision and recall in detecting PoWs.
Over 88% of PoWs can be dismissed with minimal user interaction.
Significant presence of Sneaky patterns in popular apps across China and U.S.
Abstract
In mobile applications, Pop-up window (PoW) plays a crucial role in improving user experience, guiding user actions, and delivering key information. Unfortunately, the excessive use of PoWs severely degrades the user experience. These PoWs often sneakily mislead users in their choices, employing tactics that subtly manipulate decision-making processes. In this paper, we provide the first in-depth study on the Sneaky patterns in the mobile ecosystem. Our research first highlights five distinct Sneaky patterns that compromise user experience, including text mislead, UI mislead, forced action, out of context and privacy-intrusive by default. To further evaluate the impact of such Sneaky patterns at large, we developed an automated analysis pipeline called Poker, to tackle the challenges of identifying, dismissing, and collecting diverse PoWs in real-world apps. Evaluation results showed…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Green IT and Sustainability · Personal Information Management and User Behavior
