UISCOPE: Accurate, Instrumentation-free, and Visible Attack Investigation for GUI Applications

Abstract

Existing attack investigation solutions for GUI applications suffer from a few limitations such as inaccuracy (because of the dependence explosion problem), requiring instrumentation, and providing very low visibility. Such limitations have hindered their widespread and practical deployment. In this paper, we present UISCOPE, a novel accurate, instrumentation-free, and visible attack investigation system for GUI applications. The core idea of UISCOPE is to perform causality analysis on both UI elements/events which represent users' perspective and low-level system events which provide detailed information of what happens under the hood, and then correlate system events with UI events to provide high accuracy and visibility. Long running processes are partitioned to individual UI transitions, to which low-level system events are attributed, making the results accurate. The produced graphs contain (causally related) UI elements with which users are very familiar, making them easily accessible. We deployed UISCOPE on 7 machines for a week, and also utilized UISCOPE to conduct an investigation of 6 real-world attacks. Our evaluation shows that compared to existing works, UISCOPE introduces neglibible overhead (less than 1% runtime overhead and 3.05 MB event logs per hour on average) while UISCOPE can precisely identify attack provenance while offering users thorough visibility into the attack context.