research-article

CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks

Authors:

Jong KimAuthors Info & Claims

CCS '15: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security

Pages 793 - 804

https://doi.org/10.1145/2810103.2813661

Published: 12 October 2015 Publication History

Abstract

Malicious crowdsourcing, also known as crowdturfing, has become an important security problem. However, detecting accounts performing crowdturfing tasks is challenging because human workers manage the crowdturfing accounts such that their characteristics are similar with the characteristics of normal accounts. In this paper, we propose a novel crowdturfing detection method, called CrowdTarget, that aims to detect target objects of crowdturfing tasks (e.g., post, page, and URL) not accounts performing the tasks. We identify that the manipulation patterns of target objects by crowdturfing workers are unique features to distinguish them from normal objects. We apply CrowdTarget to detect collusion-based crowdturfing services to manipulate account popularity on Twitter with artificial retweets. Evaluation results show that CrowdTarget can accurately distinguish tweets receiving crowdturfing retweets from normal tweets. When we fix the false-positive rate at 0.01, the best true-positive rate is up to 0.98.

References

[1]

Addmefast.texttthttp://addmefast.com/.

[2]

Embedded tweets.texttthttps://dev.twitter.com/web/embedded-tweets/.

[3]

Klout.texttthttps://klout.com/.

[4]

Retweets.pro.texttthttp://retweets.pro/.

[5]

Socialshop.texttthttp://socialshop.co/.

[6]

Traffup.texttthttp://traffup.net/.

[7]

Twitter reaches spam lawsuit settlement with tweet adder.texttthttp://marketingland.com/twitter-reaches-spam-ławsuit-settlement-with-tweet-adder-45890/.

[8]

A. Beutel, W. Xu, V. Guruswami, C. Palow, and C. Faloutsos. CopyCatch: Stopping group attacks by spotting lockstep behavior in social networks. In International World Wide Web Conference (WWW), 2013.

Digital Library

[9]

Y. Boshmaf, D. Logothetis, G. Siganos, J. Lería, J. Lorenzo, M. Ripeanu, and K. Beznosov. Íntegro: Leveraging victim prediction for robust fake account detection in OSNs. In Network and Distributed System Security Symposium (NDSS), 2015.

[10]

Q. Cao, M. Sirivianos, X. Yang, and T. Pregueiro. Aiding the detection of fake accounts in large scale social online services. In USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2012.

Digital Library

[11]

Q. Cao, X. Yang, J. Yu, and C. Palow. Uncovering large groups of active malicious accounts in online social networks. In ACM Conference on Computer and Communications Security (CCS), 2014.

Digital Library

[12]

Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia. Who is tweeting on Twitter: Human, bot, or cyborg? In Annual Computer Security Applications Conference (ACSAC), 2010.

Digital Library

[13]

G. Danezis and P. Mittal. SybilInfer: Detecting Sybil nodes using social networks. In Network and Distributed System Security Symposium (NDSS), 2009.

[14]

E. De Cristofaro, A. Friedman, G. Jourjon, M. A. Kaafar, and M. Z. Shafiq. Paying for likes?: Understanding Facebook like fraud using honeypots. In Internet Measurement Conference (IMC), 2014.

Digital Library

[15]

M. Egele, G. Stringhini, C. Kruegel, and G. Vigna. COMPA: Detecting compromised accounts on social networks. In Network and Distributed System Security Symposium (NDSS), 2013.

[16]

M. Jiang, P. Cui, A. Beutel, C. Faloutsos, and S. Yang. CatchSync: Catching synchronized behavior in large directed graphs. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2014.

Digital Library

[17]

K. Lee, J. Caverlee, and S. Webb. Uncovering social spammers: Social honeypots

[18]

machine learning. In International ACM SIGIR Conference on Research and Development in Information Retrieval, 2010.

[19]

K. Lee, P. Tamilarasan, and J. Caverlee. Crowdturfers, campaigns, and social media: Tracking and revealing crowdsourced manipulation of social media. In International AAAI Conference on Web and Social Media (ICWSM), 2013.

[20]

K. Lee, S. Webb, and H. Ge. The dark side of micro-task marketplaces: Characterizing Fiverr and automatically detecting crowdturfing. In International AAAI Conference on Web and Social Media (ICWSM), 2014.

[21]

M. Motoyama, D. McCoy, K. Levchenko, S. Savage, and G. M. Voelker. Dirty jobs: The role of freelance labor in web service abuse. In USENIX Security Symposium, 2011.

Digital Library

[22]

Scikit-learn.texttthttps://http://scikit-learn.org.

[23]

J. Song, S. Lee, and J. Kim. I know the shortened URLs you clicked on Twitter: Inference attack using public click analytics and Twitter metadata. In International World Wide Web Conference (WWW), 2013.

Digital Library

[24]

G. Stringhini, C. Kruegel, and G. Vigna. Detecting spammers on social networks. In Annual Computer Security Applications Conference (ACSAC), 2010.

Digital Library

[25]

G. Stringhini, G. Wang, M. Egele, C. Kruegel, G. Vigna, H. Zheng, and B. Y. Zhao. Follow the green: growth and dynamics in Twitter follower markets. In Internet Measurement Conference (IMC), 2013.

Digital Library

[26]

K. Thomas, D. McCoy, C. Grier, A. Kolcz, and V. Paxson. Trafficking fraudulent accounts: The role of the underground market in Twitter spam and abuse. In USENIX Security Symposium, 2013.

Digital Library

[27]

Twitter. State of Twitter spam.texttthttps://blog.twitter.com/2010/state-twitter-spam.

[28]

Twitter Blogs. Following rules and best practices.texttthttps://support.twitter.com/entries/68916-following-rules-and-best-practices.

[29]

Twitter Blogs. Next steps with the t.co link wrapper, 2011. https://blog.twitter.com/2011/next-steps-with-the-tco-link-wrapper.

[30]

B. Viswanath, M. A. Bashir, M. Crovella, S. Guha, K. P. Gummadi, B. Krishnamurthy, and A. Mislove. Towards detecting anomalous user behavior in online social networks. In USENIX Security Symposium, 2014.

Digital Library

[31]

B. Viswanath, A. Post, K. P. Gummadi, and A. Mislove. An analysis of social network-based Sybil defenses. In ACM SIGCOMM, 2010.

Digital Library

[32]

G. Wang, T. Konolige, C. Wilson, X. Wang, H. Zheng, and B. Y. Zhao. You are how you click: Clickstream analysis for Sybil detection. In USENIX Security Symposium, 2013.

Digital Library

[33]

G. Wang, T. Wang, H. Zheng, and B. Y. Zhao. Man vs. machine: Practical adversarial detection of malicious crowdsourcing workers. In USENIX Security Symposium, 2014.

Digital Library

[34]

G. Wang, C. Wilson, X. Zhao, Y. Zhu, M. Mohanlal, H. Zheng, and B. Y. Zhao. Serf and turf: Crowdturfing for fun and profit. In International World Wide Web Conference (WWW), 2012.

Digital Library

[35]

C. Yang, R. C. Harkreader, and G. Gu. Die free or live hard? empirical evaluation and new design for fighting evolving Twitter spammers. In Recent Advances in Intrusion Detection, pages 318--337. Springer, 2011.

Digital Library

[36]

Z. Yang, C. Wilson, X. Wang, T. Gao, B. Y. Zhao, and Y. Dai. Uncovering social network Sybils in the wild. In Internet Measurement Conference (IMC), 2011.

Digital Library

[37]

H. Yu, P. B. Gibbons, M. Kaminsky, and F. Xiao. SybilLimit: A near-optimal social network defense against Sybil attacks. In IEEE Symposium on Security and Privacy (Oakland), 2008.

Digital Library

[38]

H. Yu, M. Kaminsky, P. B. Gibbons, and A. Flaxman. SybilGuard: Defending against Sybil attacks via social networks. In ACM SIGCOMM, 2006.

Digital Library

Cited By

He PZhang XLin CWang TJi S(2024)Towards understanding bogus traffic service in online social networks在线社交网络中的虚假流量服务挖掘Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230006825:3(415-431)Online publication date: 23-Mar-2024
https://doi.org/10.1631/FITEE.2300068
Recabarren RCarbunar BHernandez NShafin ACalandrino JTroncoso C(2023)Strategies and vulnerabilities of participants in venezuelan influence operationsProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620611(6683-6700)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620611
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
Show More Cited By

Index Terms

CrowdTarget: Target-based Detection of Crowdturfing in Online Social Networks
1. Security and privacy
  1. Network security
2. Social and professional topics
  1. Computing / technology policy
    1. Computer crime

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Published In

cover image ACM Conferences

CCS '15: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security

October 2015

1750 pages

ISBN:9781450338325

DOI:10.1145/2810103

General Chair:
Indrajit Ray
Colorado State University, USA
,
Program Chairs:
Ninghui Li
Purdue University, USA
,
Christopher Kruegel
University of California, Santa Barbara, USA

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].

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Publication History

Published: 12 October 2015

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ICT R&D program of MSIP/IITP

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CCS'15

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SIGSAC

CCS'15: The 22nd ACM Conference on Computer and Communications Security

October 12 - 16, 2015

Colorado, Denver, USA

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CCS '15 Paper Acceptance Rate 128 of 660 submissions, 19%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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CCS '25

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sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

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Cited By

He PZhang XLin CWang TJi S(2024)Towards understanding bogus traffic service in online social networks在线社交网络中的虚假流量服务挖掘Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230006825:3(415-431)Online publication date: 23-Mar-2024
https://doi.org/10.1631/FITEE.2300068
Recabarren RCarbunar BHernandez NShafin ACalandrino JTroncoso C(2023)Strategies and vulnerabilities of participants in venezuelan influence operationsProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620611(6683-6700)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620611
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
Soliman WRinta-Kahila T(2023)Unethical but not illegal! A critical look at two-sided disinformation platforms: Justifications, critique, and a way forwardJournal of Information Technology10.1177/0268396223118114539:3(441-476)Online publication date: 20-Jun-2023
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Ojha AAgarwal ASwetha G(2023)Generative Adversarial Networks for Cybersecurity Threat2023 International Conference on Recent Advances in Science and Engineering Technology (ICRASET)10.1109/ICRASET59632.2023.10419878(1-6)Online publication date: 23-Nov-2023
https://doi.org/10.1109/ICRASET59632.2023.10419878
Tricomi PTarahomi SCattai CMartini FConti M(2023)Are We All in a Truman Show? Spotting Instagram Crowdturfing through Self-Training2023 32nd International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN58024.2023.10230134(1-10)Online publication date: Jul-2023
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Yin QLi QGong NXu KLiang XHu S(2023)Detecting Fake Users in Online Social NetworksAI Embedded Assurance for Cyber Systems10.1007/978-3-031-42637-7_11(201-217)Online publication date: 11-Aug-2023
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