Unveiling iOS Scamwares through Crowdturfing Reviews
Pages 399 - 404
Abstract
The iOS App Store is widely recognized as a trustworthy source for applications, primarily because of the strict regulations enforced by Apple. However, despite these measures, the presence of scamwares and the prevalence of crowdturfing (fake) reviews continue to persist within the App Store ecosystem. Previous research has primarily focused on identifying scamware through various app analysis techniques or measuring removed apps or removed reviews independently. Nevertheless, the community is still unaware of the potential impact of analyzing user reviews on enhancing scamware detection effectiveness. To address this research gap, this study conducts a large-scale investigation of crowdturfing reviews and scamwares within the iOS App Store. We first use the community detection algorithm to identify crowdturfing reviews on the user relation network. Then, based on the unique characteristics of scamwares from the perspective of crowdturfing reviews, we design three new features to assess the risk of an application. Finally, we apply machine learning algorithms to leverage our three well-designed features for scamware detection.
The experimental results obtained from our labelled benchmark dataset showcase the effectiveness of our approach, achieving a good performance (F1 score 96%+) in scamware detection. The significance of our approach lies in its practicality and universality as a scamware detector in light of the constantly evolving landscape of iOS scamwares.
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Index Terms
- Unveiling iOS Scamwares through Crowdturfing Reviews
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SEC'19: Proceedings of the 28th USENIX Conference on Security SymposiumA new type of malicious crowdsourcing (a.k.a., crowdturfing) clients, mobile apps with hidden crowdturfing user interface (UI), is increasingly being utilized by miscreants to coordinate crowdturfing workers and publish mobile-based crowdturfing tasks (...
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Published In
June 2024
728 pages
ISBN:9798400717017
DOI:10.1145/3661167
Copyright © 2024 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 18 June 2024
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EASE 2024
EASE 2024: 28th International Conference on Evaluation and Assessment in Software Engineering
June 18 - 21, 2024
Salerno, Italy
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Overall Acceptance Rate 71 of 232 submissions, 31%
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