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In recent years, the underground economy is proliferating in the mobilesystem. These underground economy apps (UEware) make profits from providingnon-compliant services, especially in sensitive areas such as gambling,pornography, and loans. Unlike traditional malware, most of them (over 80%) donot have malicious payloads. Due to their unique characteristics, existingdetection approaches cannot effectively and efficiently mitigate this emergingthreat.  To address this problem, we propose a novel approach to effectively andefficiently detect UEware by considering their UI transition graphs (UTGs).Based on the proposed approach, we design and implement a system namedDeUEDroid to perform the detection. To evaluate DeUEDroid, we collect 25,717apps and build the first large-scale ground-truth dataset (1,700 apps) ofUEware. The evaluation result based on the ground-truth dataset shows thatDeUEDroid can cover new UI features and statically construct precise UTG. Itachieves 98.22% detection F1-score and 98.97% classification accuracy,significantly outperforming traditional approaches. The evaluation involving24,017 apps demonstrates the effectiveness and efficiency of UEware detectionin real-world scenarios. Furthermore, the result reveals that UEware areprevalent, with 54% of apps in the wild and 11% of apps in app stores beingUEware. Our work sheds light on future work in analyzing and detecting UEware.

DeUEDroid: Detecting Underground Economy Apps Based on UTG Similarity