Keeping Context In Mind: Automating Mobile App Access Control with User Interface Inspection

TL;DR

COSMOS is a system that uses user interface inspection and machine learning to automatically determine if background resource access in mobile apps aligns with foreground interactions, enhancing privacy and security.

Contribution

It introduces a novel context-aware mediation system that learns from app interfaces to detect malicious or out-of-pattern background access requests in real-time.

Findings

Achieves high precision and recall in detecting malicious requests.

Effectively captures user privacy preferences through continuous learning.

Operates with low performance overhead on smartphones.

Abstract

Recent studies observe that app foreground is the most striking component that influences the access control decisions in mobile platform, as users tend to deny permission requests lacking visible evidence. However, none of the existing permission models provides a systematic approach that can automatically answer the question: Is the resource access indicated by app foreground? In this work, we present the design, implementation, and evaluation of COSMOS, a context-aware mediation system that bridges the semantic gap between foreground interaction and background access, in order to protect system integrity and user privacy. Specifically, COSMOS learns from a large set of apps with similar functionalities and user interfaces to construct generic models that detect the outliers at runtime. It can be further customized to satisfy specific user privacy preference by continuously evolving with user decisions. Experiments show that COSMOS achieves both high precision and high recall in detecting malicious requests. We also demonstrate the effectiveness of COSMOS in capturing specific user preferences using the decisions collected from 24 users and illustrate that COSMOS can be easily deployed on smartphones as a real-time guard with a very low performance overhead.