Yubo Hu | Searcharxiv

arXiv2022

DeUEDroid: Detecting Underground Economy Apps Based on UTG Similarity

In recent years, the underground economy is proliferating in the mobile system. These underground economy apps (UEware) make profits from providing non-compliant services, especially in sensitive areas such as gambling, pornography, and loans. Unlike traditional malware, most of them (over 80%) do not have malicious payloads. Due to their unique characteristics, existing detection approaches cannot effectively and efficiently mitigate this emerging threat. To address this problem, we propose a novel approach to effectively and efficiently detect UEware by considering their UI transition graphs (UTGs). Based on the proposed approach, we design and implement a system named DeUEDroid to perform the detection. To evaluate DeUEDroid, we collect 25,717 apps and build the first large-scale ground-truth dataset (1,700 apps) of UEware. The evaluation result based on the ground-truth dataset shows that DeUEDroid can cover new UI features and statically construct precise UTG. It achieves 98.22% detection F1-score and 98.97% classification accuracy, significantly outperforming traditional approaches. The evaluation involving 24,017 apps demonstrates the effectiveness and efficiency of UEware detection in real-world scenarios. Furthermore, the result reveals that UEware are prevalent, with 54% of apps in the wild and 11% of apps in app stores being UEware. Our work sheds light on future work in analyzing and detecting UEware.

cs.CR↗

arXiv2021

Lifting The Grey Curtain: A First Look at the Ecosystem of CULPRITWARE

Mobile apps are extensively involved in cyber-crimes. Some apps are malware which compromise users' devices, while some others may lead to privacy leakage. Apart from them, there also exist apps which directly make profit from victims through deceiving, threatening or other criminal actions. We name these apps as CULPRITWARE. They have become emerging threats in recent years. However, the characteristics and the ecosystem of CULPRITWARE remain mysterious. This paper takes the first step towards systematically studying CULPRITWARE and its ecosystem. Specifically, we first characterize CULPRITWARE by categorizing and comparing them with benign apps and malware. The result shows that CULPRITWARE have unique features, e.g., the usage of app generators (25.27%) deviates from that of benign apps (5.08%) and malware (0.43%). Such a discrepancy can be used to distinguish CULPRITWARE from benign apps and malware. Then we understand the structure of the ecosystem by revealing the four participating entities (i.e., developer, agent, operator and reaper) and the workflow. After that, we further reveal the characteristics of the ecosystem by studying the participating entities. Our investigation shows that the majority of CULPRITWARE (at least 52.08%) are propagated through social media rather than the official app markets, and most CULPRITWARE (96%) indirectly rely on the covert fourth-party payment services to transfer the profits. Our findings shed light on the ecosystem, and can facilitate the community and law enforcement authorities to mitigate the threats. We will release the source code of our tools to engage the community.

cs.CR↗