DARKFLEECE: Probing the Dark Side of Android Subscription Apps

Authors: 

Chang Yue, Institute of Information Engineering, Chinese Academy of Sciences, China; School of Cyber Security, University of Chinese Academy of Sciences, China; Chen Zhong, University of Tampa, USA; Kai Chen and Zhiyu Zhang, Institute of Information Engineering, Chinese Academy of Sciences, China; School of Cyber Security, University of Chinese Academy of Sciences, China; Yeonjoon Lee, Hanyang University, Ansan, Republic of Korea

Abstract: 

Fleeceware, a novel category of malicious subscription apps, is increasingly tricking users into expensive subscriptions, leading to substantial financial consequences. These apps' ambiguous nature, closely resembling legitimate subscription apps, complicates their detection in app markets. To address this, our study aims to devise an automated method, named DARKFLEECE, to identify fleeceware through their prevalent use of dark patterns. By recruiting domain experts, we curated the first-ever fleeceware feature library, based on dark patterns extracted from user interfaces (UI). A unique extraction method, which integrates UI elements, layout, and multifaceted extraction rules, has been developed. DARKFLEECE boasts a detection accuracy of 93.43% on our dataset and utilizes Explainable Artificial Intelligence (XAI) to present user-friendly alerts about potential fleeceware risks. When deployed to assess Google Play's app landscape, DARKFLEECE examined 13,597 apps and identified an alarming 75.21% of 589 subscription apps that displayed different levels of fleeceware, totaling around 5 billion downloads. Our results are consistent with user reviews on Google Play. Our detailed exploration into the implications of our results for ethical app developers, app users, and app market regulators provides crucial insights for different stakeholders. This underscores the need for proactive measures against the rise of fleeceware.

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