2019
DOI: 10.1109/tdsc.2019.2950253
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Understanding Illicit UI in iOS apps Through Hidden UI Analysis

Abstract: In Chameleon apps, benign UIs are displayed during Apple App vetting while their hidden potentially-harmful illicit UIs (PHI-UI) are revealed once they reached App Store. In this paper, we report the first systematic study on iOS Chameleon apps, which sheds light on a largely overlooked threat that the illicit activities are launched solely based on UI. Our research employed CHAMELEON-HUNTER, a new static analysis approach that determines the suspiciousness of a PHI-UI leveraging the semantic features generate… Show more

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Cited by 2 publications
(6 citation statements)
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“…In our study, we not only further measure the activity of crowdturfing reviews in the App Store but also combine the extracted features with multiple machine learning methods to obtain an effective detection model. The authors of [2] discovered and measured crowdturfing content hidden behind the UIs of apps and proposed a new triage methodology to identify iOS apps that may contain hidden crowdturfing UIs. They focused more on program analysis and semantic analysis in the UI and how crowdturfing blends into the app markets by hiding in the apps.…”
Section: Related Workmentioning
confidence: 99%
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“…In our study, we not only further measure the activity of crowdturfing reviews in the App Store but also combine the extracted features with multiple machine learning methods to obtain an effective detection model. The authors of [2] discovered and measured crowdturfing content hidden behind the UIs of apps and proposed a new triage methodology to identify iOS apps that may contain hidden crowdturfing UIs. They focused more on program analysis and semantic analysis in the UI and how crowdturfing blends into the app markets by hiding in the apps.…”
Section: Related Workmentioning
confidence: 99%
“…mturk.com, accessed on 7 July 2023) and Microworkers (https://www.microworkers.com, accessed on 7 July 2023). Crowdturfing leverages the crowdsourcing operation by hiring a lot of low-paid workers to perform suspicious and malicious tasks online [2]. The purpose of crowdturfing is to increase the exposure and visibility of the item, thus improving the ranking in the search engines and attracting more genuine users to find and visit it, e.g., posting fake reviews, reposting and likes on social media, and falsifying the number of downloads and installations in mobile application markets.…”
Section: Introductionmentioning
confidence: 99%
“…Previous approaches [51,52,59] directly perform data flow analysis on assembly/IR code, thus they cannot resolve the target of indirect jumps. As a result, some data flow paths cannot be correctly resolved, leading to missing some VC transitions.…”
Section: Identifying Screen Transitionsmentioning
confidence: 99%
“…Table 2 lists the experiment results. Cruiser represents the model we generated by following the previous approaches (i.e, analyzing the assembly code) [51,59]. "Transition" column represents the screen transitions discovered by tools.…”
Section: Rq1: Model Constructionmentioning
confidence: 99%
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