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Characterizing and detecting malicious crowdsourcing

Published: 27 August 2013 Publication History

Abstract

Popular Internet services in recent years have shown that remarkable things can be achieved by harnessing the power of the masses. However, crowd-sourcing systems also pose a real challenge to existing security mechanisms deployed to protect Internet services, particularly those tools that identify malicious activity by detecting activities of automated programs such as CAPTCHAs.
In this work, we leverage access to two large crowdturfing sites to gather a large corpus of ground-truth data generated by crowdturfing campaigns. We compare and contrast this data with "organic" content generated by normal users to identify unique characteristics and potential signatures for use in real-time detectors. This poster describes first steps taken focused on crowdturfing campaigns targeting the Sina Weibo microblogging system. We describe our methodology, our data (over 290K campaigns, 34K worker accounts, 61 million tweets...), and some initial results.

References

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Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., and Zhao, B. Y. Detecting and characterizing social spam campaigns. In Proc. of IMC (2010).
[2]
Wang, G., Konolige, T., Wilson, C., Wang, X., Zheng, H., and Zhao, B. Y. You are how you click: Clickstream analysis for sybil detection. In Proc. of USENIX Security (Washington, D.C., August 2013).
[3]
Wang, G., Mohanlal, M., Wilson, C., Wang, X., Metzger, M., Zheng, H., and Zhao, B. Y. Social turing tests: Crowdsourcing sybil detection. In Proc. of NDSS (San Diego, CA, February 2013).
[4]
Wang, G., Wilson, C., Zhao, X., Zhu, Y., Mohanlal, M., Zheng, H., and Zhao, B. Y. Serf and turf: Crowdturfing for fun and profit. In Proc. of WWW (2012).
[5]
Yang, Z., Wilson, C., Wang, X., Gao, T., Zhao, B. Y., and Dai, Y. Uncovering social network sybils in the wild. In Proc. of IMC (2011).

Cited By

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  • (2022)Online Social Networks Misuse, Cyber Crimes, and Counter MechanismsResearch Anthology on Combating Cyber-Aggression and Online Negativity10.4018/978-1-6684-5594-4.ch023(403-423)Online publication date: 4-Mar-2022
  • (2021)Online Social Networks Misuse, Cyber Crimes, and Counter MechanismsAnalyzing Global Social Media Consumption10.4018/978-1-7998-4718-2.ch010(183-203)Online publication date: 2021
  • (2021)Online Social Deception and Its Countermeasures: A SurveyIEEE Access10.1109/ACCESS.2020.30473379(1770-1806)Online publication date: 2021
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  1. Characterizing and detecting malicious crowdsourcing

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    Published In

    cover image ACM SIGCOMM Computer Communication Review
    ACM SIGCOMM Computer Communication Review  Volume 43, Issue 4
    October 2013
    595 pages
    ISSN:0146-4833
    DOI:10.1145/2534169
    Issue’s Table of Contents
    • cover image ACM Conferences
      SIGCOMM '13: Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
      August 2013
      580 pages
      ISBN:9781450320566
      DOI:10.1145/2486001
    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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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 August 2013
    Published in SIGCOMM-CCR Volume 43, Issue 4

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    Author Tags

    1. crowdturfing
    2. malicious crowdsourcing
    3. user behavior

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    View all
    • (2022)Online Social Networks Misuse, Cyber Crimes, and Counter MechanismsResearch Anthology on Combating Cyber-Aggression and Online Negativity10.4018/978-1-6684-5594-4.ch023(403-423)Online publication date: 4-Mar-2022
    • (2021)Online Social Networks Misuse, Cyber Crimes, and Counter MechanismsAnalyzing Global Social Media Consumption10.4018/978-1-7998-4718-2.ch010(183-203)Online publication date: 2021
    • (2021)Online Social Deception and Its Countermeasures: A SurveyIEEE Access10.1109/ACCESS.2020.30473379(1770-1806)Online publication date: 2021
    • (2020)Disruption and Deception in CrowdsourcingInternational Journal of e-Collaboration10.4018/IJeC.201710010213:4(23-41)Online publication date: 1-Oct-2020
    • (2020)Privacy in Crowdsourcing: a Review of the Threats and ChallengesComputer Supported Cooperative Work (CSCW)10.1007/s10606-020-09374-0Online publication date: 3-Feb-2020
    • (2019)Disruption and Deception in CrowdsourcingCrowdsourcing10.4018/978-1-5225-8362-2.ch011(215-235)Online publication date: 2019
    • (2019)Disruption and Deception in CrowdsourcingSocial Entrepreneurship10.4018/978-1-5225-8182-6.ch037(722-742)Online publication date: 2019
    • (2019)Solving the Crowdsourcing Dilemma Using the Zero-Determinant StrategiesIEEE Transactions on Information Forensics and Security10.1109/TIFS.2019.2949440(1-1)Online publication date: 2019
    • (2019)Uncovering the predictors of unsafe computing behaviors in online crowdsourcing contextsComputers & Security10.1016/j.cose.2019.05.00185(300-312)Online publication date: Aug-2019
    • (2019)Co-Detection of crowdturfing microblogs and spammers in online social networksWorld Wide Web10.1007/s11280-019-00727-4Online publication date: 23-Oct-2019
    • Show More Cited By

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