research-article

AppWatcher: unveiling the underground market of trading mobile app reviews

Authors:

Sencun ZhuAuthors Info & Claims

WiSec '15: Proceedings of the 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks

Article No.: 10, Pages 1 - 11

https://doi.org/10.1145/2766498.2766510

Published: 22 June 2015 Publication History

Abstract

Driven by huge monetary reward, some mobile application (app) developers turn to the underground market to buy positive reviews instead of doing legal advertisements. These promotion reviews are either directly posted in app stores like iTunes and Google Play, or published on some popular websites that have many app users. Until now, a clear understanding of this app promotion underground market is still lacking. In this work, we focus on unveiling this underground market and statistically analyzing the promotion incentives, characteristics of promoted apps and suspicious reviewers. To collect promoted apps, we built an automatic data collection system, AppWatcher, which monitored 52 paid review service providers for four months and crawled all the app metadata from their corresponding app stores. Finally, AppWatcher exposes 645 apps promoted in app stores and 29, 680 apps promoted in some popular websites. The current underground market is then reported from various perspectives (e.g., service price, app volume). We identified some interesting features of both promoted apps and suspicious reviewers, which are significantly different from those of randomly chosen apps. Finally, we built a simple tracer to narrow down the suspect list of promoted apps in the underground market.

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Cited By

Zhang YWang HStavrou A(2024)A multiview clustering framework for detecting deceptive reviewsJournal of Computer Security10.3233/JCS-22000132:1(31-52)Online publication date: 2-Feb-2024
https://doi.org/10.3233/JCS-220001
Hu QZhang XLi FTang ZWang S(2023)Measuring and Understanding Crowdturfing in the App StoreInformation10.3390/info1407039314:7(393)Online publication date: 11-Jul-2023
https://doi.org/10.3390/info14070393
Fassl MAnell SHouy SLindorfer MKrombholz KChiasson SKapadia A(2022)Comparing user perceptions of anti-stalkerware apps with the technical realityProceedings of the Eighteenth USENIX Conference on Usable Privacy and Security10.5555/3563609.3563617(135-154)Online publication date: 8-Aug-2022
https://dl.acm.org/doi/10.5555/3563609.3563617
Show More Cited By

Index Terms

AppWatcher: unveiling the underground market of trading mobile app reviews

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cover image ACM Conferences

WiSec '15: Proceedings of the 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks

June 2015

256 pages

ISBN:9781450336239

DOI:10.1145/2766498

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGSAC: ACM Special Interest Group on Security, Audit, and Control
US Army Research Office: US Army Research Office
NSF: National Science Foundation

In-Cooperation

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

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National Science Foundation

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WiSec'15

Sponsor:

SIGSAC
US Army Research Office
NSF

WiSec'15: 8th ACM Conference on Security & Privacy in Wireless and Mobile Networks

June 22 - 26, 2015

New York, New York

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Cited By

Zhang YWang HStavrou A(2024)A multiview clustering framework for detecting deceptive reviewsJournal of Computer Security10.3233/JCS-22000132:1(31-52)Online publication date: 2-Feb-2024
https://doi.org/10.3233/JCS-220001
Hu QZhang XLi FTang ZWang S(2023)Measuring and Understanding Crowdturfing in the App StoreInformation10.3390/info1407039314:7(393)Online publication date: 11-Jul-2023
https://doi.org/10.3390/info14070393
Fassl MAnell SHouy SLindorfer MKrombholz KChiasson SKapadia A(2022)Comparing user perceptions of anti-stalkerware apps with the technical realityProceedings of the Eighteenth USENIX Conference on Usable Privacy and Security10.5555/3563609.3563617(135-154)Online publication date: 8-Aug-2022
https://dl.acm.org/doi/10.5555/3563609.3563617
Hernandez NRecabarren RCarbunar BAhmed SLevin DMislove AAmann JLuckie M(2021)RacketStoreProceedings of the 21st ACM Internet Measurement Conference10.1145/3487552.3487837(639-657)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3487552.3487837
Hu YWang HZhou YGuo YLi LLuo BXu F(2021)Dating with Scambots: Understanding the Ecosystem of Fraudulent Dating ApplicationsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2019.290893918:3(1033-1050)Online publication date: 1-May-2021
https://doi.org/10.1109/TDSC.2019.2908939
Marshall JDunstan DBartik W(2020)Treating Psychological Trauma in the Midst of COVID-19: The Role of Smartphone AppsFrontiers in Public Health10.3389/fpubh.2020.004028Online publication date: 18-Aug-2020
https://doi.org/10.3389/fpubh.2020.00402
Marshall JDunstan DBartik W(2020)Effectiveness of Using Mental Health Mobile Apps as Digital Antidepressants for Reducing Anxiety and Depression: Protocol for a Multiple Baseline Across-Individuals DesignJMIR Research Protocols10.2196/171599:7(e17159)Online publication date: 5-Jul-2020
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Du KYang HZhang YDuan HWang HHao SLi ZYang M(2020)Understanding Promotion-as-a-Service on GitHubProceedings of the 36th Annual Computer Security Applications Conference10.1145/3427228.3427258(597-610)Online publication date: 7-Dec-2020
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Karunanayake NRajasegaran JGunathillake ASeneviratne SJourjon G(2020)A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play StoreIEEE Transactions on Mobile Computing10.1109/TMC.2020.3007260(1-1)Online publication date: 2020
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Levine DCo ZNewmark LGroisser AHolmgren AHaas JBates D(2020)Design and testing of a mobile health application rating toolnpj Digital Medicine10.1038/s41746-020-0268-93:1Online publication date: 21-May-2020
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