research-article

Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter

Authors:

Robert Harkreader,

Guofei GuAuthors Info & Claims

WWW '12: Proceedings of the 21st international conference on World Wide Web

Pages 71 - 80

https://doi.org/10.1145/2187836.2187847

Published: 16 April 2012 Publication History

Abstract

In this paper, we perform an empirical analysis of the cyber criminal ecosystem on Twitter. Essentially, through analyzing inner social relationships in the criminal account community, we find that criminal accounts tend to be socially connected, forming a small-world network. We also find that criminal hubs, sitting in the center of the social graph, are more inclined to follow criminal accounts. Through analyzing outer social relationships between criminal accounts and their social friends outside the criminal account community, we reveal three categories of accounts that have close friendships with criminal accounts. Through these analyses, we provide a novel and effective criminal account inference algorithm by exploiting criminal accounts' social relationships and semantic coordinations.

References

[1]

Automatically Follow Back. http://autofollowback.com.

[2]

Calculate Twitter Reputation. http://www.thekirankumar.com/blog/tag/calculate-twitter-reputation/.

[3]

Capture HPC. https://projects.honeynet.org/capture-hpc.

[4]

Google Safe Browsing API. http://code.google.com/apis/safebrowsing/.

[5]

KOOBFACE: Inside a Crimeware Network. http://www.infowar-monitor.net/reports/iwm-koobface.pdf.

[6]

Lady Gaga Falls Prey to Rogue Twitter Attack. http://mashable.com/2011/04/28/lady-gaga-twitter-attack/.

[7]

Purchase Twitter Friends. http://www.purchasetwitterfriends.com/.

[8]

The Twitter Rules. http://help.twitter.com/entries/18311-the-twitter-rules.

[9]

Twitter accounts spreading malicious code. http://www.net-security.org/malware_news.php?id=1554.

[10]

Twitter-based Botnet Command Channel. http://asert.arbornetworks.com/2009/08/twitter-based-botnet-command-channel/.

[11]

Twitter Streaming API. https://dev.twitter.com/docs/streaming-api.

[12]

Twitter vulnerability allows cyber criminals to spread spam. http://www.one.com/en/web-hosting-news/website/twitter-vulnerability-allows-/cyber-criminals-to-spread-spam-links\$800076628.htm.

[13]

Twitter's Following Limits. http://support.twitter.com/groups/32-something-s-not-working/topics/117-following-problems/articles/66885-i-can-t-follow-people-follow-limits.

[14]

F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida. Detecting Spammers on Twitter. In Collaboration, Electronic messaging, Anti-Abuse and Spam Confference (CEAS), 2010.

[15]

F. Benevenuto, T. Rodrigues, V. Almeida, J. Almeida, C. Zhang, and K. Ross. Identifying Video Spammers in Online Social Networks. In Int'l Workshop on Adversarial Information Retrieval on the Web (AirWeb'08), 2008.

Digital Library

[16]

F. Benevenuto, T. Rodrigues, V. Almeida, J. Almeida, C. Zhang, and K. Ross. Detecting Spammers and Content Promoters in Online Video Social Networks. In ACM SIGIR Conference (SIGIR'09), 2009.

Digital Library

[17]

C. Castillo, M. Mendoza, and B. Poblete. Information Credibility on Twitter. In International World Wide Web Conference, (WWW'11), 2011.

Digital Library

[18]

M. Cha, H. Haddadi, F. Benevenuto, and K. Gummadi. Measuring User Influence in Twitter: The Million Follower Fallacy. In Int'l AAAI Conference on Weblogs and Social Media (ICWSM), 2010.

[19]

G. Danezis and P. Mittal. SybilInfer: Detecting Sybil Nodes using Social Networks. In 16th Annual Network and Distributed System Security Symposium, 2009.

[20]

W. Galuba, K. Aberer, D. Chakraborty, Z. Despotovic, and W. Kellerer. Outtweeting the Twitterers - Predicting Information Cascades in Microblogs. In Usenix Workshop on Online Social Networks, (WOSN'10), 2010.

Digital Library

[21]

H. Gao, J. Hu, C. Wilson, Z. Li, Y. Chen, and B. Zhao. Detecting and Characterizing Social Spam Campaigns. In Proceeding of ACM SIGCOMM IMC(ICM'10), 2010.

Digital Library

[22]

C. Grier, K. Thomas, V. Paxsony, and M. Zhangy. @spam: The Underground on 140 Characters or Less. In ACM Conference on Computer and Communications Security (CCS), 2010.

Digital Library

[23]

J. Kleinberg. Authoritative sources in a hyperlinked environment. In Journal of the ACM, Vol.46, No. 5, pp. 604--632, 1999.

Digital Library

[24]

G. Koutrika, F. Effendi, Z. Gyongyi, P. Heymann, and H. Garcia-Molina. Combating spam in tagging systems. In Int'l Workshop on Adversarial Information Retrieval on the Web (AIRWeb'07), 2007.

Digital Library

[25]

H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a Social Network or a News Media? In Int'l World Wide Web (WWW '10), 2010.

Digital Library

[26]

K. Lee, J. Caverlee, and S. Webb. Uncovering Social Spammers: Social Honeypots Machine Learning. In ACM SIGIR Conference (SIGIR), 2010.

Digital Library

[27]

P. Metaxas and E. Mustafaraj. Prominence in minutes: Political speech and real-time search. In In Proceedings of the Web Science 2010, 2010.

[28]

A. Mislove, M. Marcon, K. Gummadi, P. Druschel, and B. Bhattacharjee. Outtweeting the Twitterers - Predicting Information Cascades in Microblogs. In Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, (IMC'07), 2007.

[29]

D. Pelleg and A. Moore. X-Means: Extending K-means with Efficient Estimation. In International Conference on Machine Learning, 2000.

Digital Library

[30]

J. Ratkiewicz, M. Conover, M. Meiss, B. Goncalves, S. Patil, A. Flammini, and F. Menczer. Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams. In Proceedings of 5th International Conference on Weblogs and Social Media, 2011.

[31]

G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. In Information Processing & Management, 1998.

Digital Library

[32]

G. Stringhini, S. Barbara, C. Kruegel, and G. Vigna. Detecting Spammers On Social Networks. In Annual Computer Security Applications Conference, 2010.

Digital Library

[33]

A. Wang. Don't follow me: spam detecting in Twitter. In Int'l Conferene on Security and Cryptography (SECRYPT), 2010.

[34]

T. Wang, Y. Chen, Z. Zhang, P. Sun, B. Deng, and X. Li. Unbiased Sampling in Directed Social Graph. In ACM Special Interest Group on Data Communication, (SIGCOMM'10), 2010.

Digital Library

[35]

C. Yang, R. Harkreader, and G. Gu. Die Free or Live Hard? Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers. In In Proceedings of the 14th International Symposium on Recent Advances in Intrusion Detection (RAID'11), 2011.

Digital Library

[36]

S. Yardi, D. Romero, G. Schoenebeck, and D. Boyd. Detecting spam in a Twitter network. In First Monday, 15(1), 2010.

[37]

H. Yu, M. Kaminsky, P. Gibbons, and A. Flaxman. SybilGuard: Defending Against Sybil Attacks via Social Networks. In Proceedings of ACM SIGCOMM Conference, 2006.

Digital Library

[38]

J. Zhang, P. Porras, and J. Ullrich. Highly predictive blacklisting. In 17th USENIX Security Symposium (USENIX Security'08), 2008.

Digital Library

Cited By

Mohamed AAlnor NMohammed OAl-Matari EAlhebri AAhmed A(2024)The impact of information technology governance according to the COBIT on performanceInternational Journal of ADVANCED AND APPLIED SCIENCES10.21833/ijaas.2024.03.01411:3(127-136)Online publication date: Mar-2024
https://doi.org/10.21833/ijaas.2024.03.014
NAKANO HCHIBA DKOIDE TFUKUSHI NYAGI THARIU TYOSHIOKA KMATSUMOTO T(2024)Understanding Characteristics of Phishing Reports from Experts and Non-Experts on TwitterIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7221E107.D:7(807-824)Online publication date: 1-Jul-2024
https://doi.org/10.1587/transinf.2023EDP7221
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Index Terms

Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter

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Published In

cover image ACM Other conferences

WWW '12: Proceedings of the 21st international conference on World Wide Web

April 2012

1078 pages

ISBN:9781450312295

DOI:10.1145/2187836

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Copyright © 2012 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 ACM 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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New York, NY, United States

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Published: 16 April 2012

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WWW 2012

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Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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Cited By

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https://doi.org/10.21833/ijaas.2024.03.014
NAKANO HCHIBA DKOIDE TFUKUSHI NYAGI THARIU TYOSHIOKA KMATSUMOTO T(2024)Understanding Characteristics of Phishing Reports from Experts and Non-Experts on TwitterIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7221E107.D:7(807-824)Online publication date: 1-Jul-2024
https://doi.org/10.1587/transinf.2023EDP7221
Liu XFan XMa RChen KLi YWang GXu WSerra ESpezzano F(2024)Collaborative Fraud Detection on Large Scale Graph Using Secure Multi-Party ComputationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679863(1473-1482)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679863
Tsuchiya TCuevas AChristin NChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Identifying Risky Vendors in Cryptocurrency P2P MarketplacesProceedings of the ACM Web Conference 202410.1145/3589334.3645475(99-110)Online publication date: 13-May-2024
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Kalpana PMalleboina KNikhitha MSaikiran PKumar S(2024)Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithm2024 International Conference on Data Science and Network Security (ICDSNS)10.1109/ICDSNS62112.2024.10691297(1-7)Online publication date: 26-Jul-2024
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Tripathi AGhosh MBharti K(2024)Markov enhanced graph attention network for spammer detection in online social networkKnowledge and Information Systems10.1007/s10115-024-02137-z66:9(5561-5580)Online publication date: 29-May-2024
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Rosaline RVinod DNancy PAnitha KDevipriya A(2024)Design and Development of Techniques for Fake Profile Detection in Online Social NetworksOnline Social Networks in Business Frameworks10.1002/9781394231126.ch13(319-336)Online publication date: 20-Sep-2024
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Dhaka DMehrotra M(2023)A Comprehensive Study to Detect Social Spam CampaignsAdvanced Applications of NLP and Deep Learning in Social Media Data10.4018/978-1-6684-6909-5.ch001(1-18)Online publication date: 9-Jun-2023
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