research-article

AdRob: examining the landscape and impact of android application plagiarism

Authors:

Jonathan Crussell,

Heesook ChoiAuthors Info & Claims

MobiSys '13: Proceeding of the 11th annual international conference on Mobile systems, applications, and services

Pages 431 - 444

https://doi.org/10.1145/2462456.2464461

Published: 25 June 2013 Publication History

Abstract

Malicious activities involving Android applications are rising rapidly. As prior work on cyber-crimes suggests, we need to understand the economic incentives of the criminals to design the most effective defenses. In this paper, we investigate application plagiarism on Android markets at a large scale. We take the first step to characterize plagiarized applications and estimate their impact on the original application developers. We first crawled 265,359 free applications from 17 Android markets around the world and ran a tool to identify similar applications ("clones"). Based on the data, we examined properties of the cloned applications, including their distribution across different markets, application categories, and ad libraries. Next, we examined how cloned applications affect the original developers. We captured HTTP advertising traffic generated by mobile applications at a tier-1 US cellular carrier for 12 days. To associate each Android application with its advertising traffic, we extracted a unique advertising identifier (called the client ID) from both the applications and the network traces. We estimate a lower bound on the advertising revenue that cloned applications siphon from the original developers, and the user base that cloned applications divert from the original applications. To the best of our knowledge, this is the first large scale study on the characteristics of cloned mobile applications and their impact on the original developers.

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Cited By

Xinyu LZe JJiaxi LWei LXiaoxi WQixu L(2023)ANDetect: A Third-party Ad Network Libraries Detection Framework for Android ApplicationsProceedings of the 39th Annual Computer Security Applications Conference10.1145/3627106.3627182(98-112)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3627106.3627182
Lv PLi PZhang SChen KLiang RMa HZhao YLi Y(2023)A Robustness-Assured White-Box Watermark in Neural NetworksIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.324273720:6(5214-5229)Online publication date: Nov-2023
https://doi.org/10.1109/TDSC.2023.3242737
Alhashmi SAlneyadi AAlshehhi MLamaazi H(2023)Mobile and Web Applications Clones: A Comprehensive Study2023 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC58020.2023.10182983(464-469)Online publication date: 19-Jun-2023
https://doi.org/10.1109/IWCMC58020.2023.10182983
Show More Cited By

Index Terms

AdRob: examining the landscape and impact of android application plagiarism
1. Social and professional topics
  1. Computing / technology policy
    1. Computer crime

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Published In

cover image ACM Conferences

MobiSys '13: Proceeding of the 11th annual international conference on Mobile systems, applications, and services

June 2013

568 pages

ISBN:9781450316729

DOI:10.1145/2462456

General Chairs:
Hao-Hua Chu
National Taiwan University
,
Polly Huang
National Taiwan University
,
Program Chairs:
Romit Roy Choudhury
Duke University
,
Feng Zhao
Microsoft Research

Copyright © 2013 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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

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SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2013

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MobiSys'13

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SIGMOBILE

MobiSys'13: The 11th Annual International Conference on Mobile Systems, Applications, and Services

June 25 - 28, 2013

Taipei, Taiwan

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MobiSys '13 Paper Acceptance Rate 33 of 211 submissions, 16%;

Overall Acceptance Rate 274 of 1,679 submissions, 16%

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Cited By

Xinyu LZe JJiaxi LWei LXiaoxi WQixu L(2023)ANDetect: A Third-party Ad Network Libraries Detection Framework for Android ApplicationsProceedings of the 39th Annual Computer Security Applications Conference10.1145/3627106.3627182(98-112)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3627106.3627182
Lv PLi PZhang SChen KLiang RMa HZhao YLi Y(2023)A Robustness-Assured White-Box Watermark in Neural NetworksIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2023.324273720:6(5214-5229)Online publication date: Nov-2023
https://doi.org/10.1109/TDSC.2023.3242737
Alhashmi SAlneyadi AAlshehhi MLamaazi H(2023)Mobile and Web Applications Clones: A Comprehensive Study2023 International Wireless Communications and Mobile Computing (IWCMC)10.1109/IWCMC58020.2023.10182983(464-469)Online publication date: 19-Jun-2023
https://doi.org/10.1109/IWCMC58020.2023.10182983
Rafiq HAslam NAleem MIssac BRandhawa R(2022)AndroMalPack: enhancing the ML-based malware classification by detection and removal of repacked apps for Android systemsScientific Reports10.1038/s41598-022-23766-w12:1Online publication date: 14-Nov-2022
https://doi.org/10.1038/s41598-022-23766-w
Saifan AAlzyoud A(2020)Mutation Testing to Evaluate Android ApplicationsInternational Journal of Open Source Software and Processes10.4018/IJOSSP.202001010211:1(23-40)Online publication date: 1-Jan-2020
https://doi.org/10.4018/IJOSSP.2020010102
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
https://doi.org/10.1109/TMC.2020.3007260
Xu JYuan Q(2020)LibRoad: Rapid, Online, and Accurate Detection of TPLs on AndroidIEEE Transactions on Mobile Computing10.1109/TMC.2020.3003336(1-1)Online publication date: 2020
https://doi.org/10.1109/TMC.2020.3003336
Guo JLiu DZhao RLi Z(2020)WLTDroid: Repackaging Detection Approach for Android ApplicationsWeb Information Systems and Applications10.1007/978-3-030-60029-7_52(579-591)Online publication date: 22-Sep-2020
https://doi.org/10.1007/978-3-030-60029-7_52
Rajasegaran JKarunanayake NGunathillake ASeneviratne SJourjon G(2019)A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit AppsThe World Wide Web Conference10.1145/3308558.3313427(3165-3171)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313427
Wu TDeng XYan JZhang J(2019)Analyses for specific defects in android applicationsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-018-7008-113:6(1210-1227)Online publication date: 1-Dec-2019
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