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Graph-Based Fraud Detection in the Face of Camouflage

Authors:

Christos FaloutsosAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 4

Article No.: 44, Pages 1 - 26

https://doi.org/10.1145/3056563

Published: 29 June 2017 Publication History

Abstract

Given a bipartite graph of users and the products that they review, or followers and followees, how can we detect fake reviews or follows? Existing fraud detection methods (spectral, etc.) try to identify dense subgraphs of nodes that are sparsely connected to the remaining graph. Fraudsters can evade these methods using camouflage, by adding reviews or follows with honest targets so that they look “normal.” Even worse, some fraudsters use hijacked accounts from honest users, and then the camouflage is indeed organic.

Our focus is to spot fraudsters in the presence of camouflage or hijacked accounts. We propose FRAUDAR, an algorithm that (a) is camouflage resistant, (b) provides upper bounds on the effectiveness of fraudsters, and (c) is effective in real-world data. Experimental results under various attacks show that FRAUDAR outperforms the top competitor in accuracy of detecting both camouflaged and non-camouflaged fraud. Additionally, in real-world experiments with a Twitter follower--followee graph of 1.47 billion edges, FRAUDAR successfully detected a subgraph of more than 4, 000 detected accounts, of which a majority had tweets showing that they used follower-buying services.

References

[1]

Leman Akoglu, Rishi Chandy, and Christos Faloutsos. 2013. Opinion fraud detection in online reviews by network effects. In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media.

[2]

Alex Beutel, Kenton Murray, Christos Faloutsos, and Alexander J. Smola. 2014. Cobafi: Collaborative Bayesian filtering. In Proceedings of the 23rd International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 97--108.

Digital Library

[3]

Alex Beutel, Wanhong Xu, Venkatesan Guruswami, Christopher Palow, and Christos Faloutsos. 2013. Copycatch: Stopping group attacks by spotting lockstep behavior in social networks. In Proceedings of the 22nd International Conference on World Wide Web. International World Wide Web Conferences Steering Committee. 119--130.

Digital Library

[4]

Shankar Bhamidi, J. Michael Steele, Tauhid Zaman, and others. 2015. Twitter event networks and the superstar model. The Annals of Applied Probability 25, 5 (2015), 2462--2502.

[5]

Qiang Cao, Michael Sirivianos, Xiaowei Yang, and Tiago Pregueiro. 2012. Aiding the detection of fake accounts in large scale social online services. In Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation.

Digital Library

[6]

Moses Charikar. 2000. Greedy approximation algorithms for finding dense components in a graph. In Approximation Algorithms for Combinatorial Optimization. Springer, 84--95.

Digital Library

[7]

Corinna Cortes, Daryl Pregibon, and Chris Volinsky. 2001. Communities of Interest. Springer.

[8]

Saptarshi Ghosh, Bimal Viswanath, Farshad Kooti, Naveen Kumar Sharma, Gautam Korlam, Fabricio Benevenuto, Niloy Ganguly, and Krishna Phani Gummadi. 2012. Understanding and combating link farming in the twitter social network. In Proceedings of the 21st International Conference on World Wide Web. ACM, 61--70.

Digital Library

[9]

Christos Giatsidis, Dimitrios M. Thilikos, and Michalis Vazirgiannis. 2011. Evaluating cooperation in communities with the k-core structure. In Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 87--93.

Digital Library

[10]

Zhongshu Gu, Kexin Pei, Qifan Wang, Luo Si, Xiangyu Zhang, and Dongyan Xu. 2015. LEAPS: Detecting camouflaged attacks with statistical learning guided by program analysis. In Proceedings of 2015 45th Annual IEEE/IFIP International Conference on Dependable Systems and Networks. IEEE, 57--68.

Digital Library

[11]

Zoltán Gyöngyi, Hector Garcia-Molina, and Jan Pedersen. 2004. Combating web spam with trustrank. In Proceedings of the 30th International Conference on Very Large Data Bases. 576--587.

Digital Library

[12]

Meng Jiang, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, and Christos Faloutsos. 2015. A general suspiciousness metric for dense blocks in multimodal data. In Proceedings of the 2015 IEEE International Conference on Data Mining (ICDM). IEEE, 781--786.

Digital Library

[13]

Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, and Shiqiang Yang. 2014b. CatchSync: Catching synchronized behavior in large directed graphs. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 941--950.

Digital Library

[14]

Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, and Shiqiang Yang. 2014a. Inferring strange behavior from connectivity pattern in social networks. In Advances in Knowledge Discovery and Data Mining. Springer, 126--138.

[15]

Nitin Jindal and Bing Liu. 2008. Opinion spam and analysis. In Proceedings of the 2008 International Conference on Web Search and Data Mining 2008. ACM, 219--230.

Digital Library

[16]

Chris Kanich, Christian Kreibich, Kirill Levchenko, Brandon Enright, Geoffrey M. Voelker, Vern Paxson, and Stefan Savage. 2008. Spamalytics: An empirical analysis of spam marketing conversion. In Proceedings of the 15th ACM Conference on Computer and Communications Security. ACM, 3--14.

Digital Library

[17]

Chris Kanich, Nicholas Weaver, Damon McCoy, Tristan Halvorson, Christian Kreibich, Kirill Levchenko, Vern Paxson, Geoffrey M. Voelker, and Stefan Savage. 2011. Show me the money: Characterizing spam-advertised revenue. In Proceedings of the 20th USENIX Security Symposium. 15--15.

Digital Library

[18]

G. Karypis and V. Kumar. 1995. METIS: Unstructured graph partitioning and sparse matrix ordering system, Version 2. The University of Minnesota.

[19]

J. M. Kleinberg. 1999. Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM) 46, 5 (1999), 604--632.

Digital Library

[20]

Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is twitter, a social network or a news media? In Proceedings of the 19th International Conference on World Wide Web. ACM, 591--600.

Digital Library

[21]

Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Signed networks in social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1361--1370.

Digital Library

[22]

Julian McAuley and Jure Leskovec. 2013. Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proceedings of the 7th ACM Conference on Recommender Systems. ACM, 165--172.

Digital Library

[23]

Bhaskar Mehta and Thomas Hofmann. 2008. A survey of attack-resistant collaborative filtering algorithms. IEEE Technical Committee on Data Engineering 31, 2 (2008), 14--22.

[24]

Bhaskar Mehta, Thomas Hofmann, and Wolfgang Nejdl. 2007. Robust collaborative filtering. In Proceedings of the 2007 ACM Conference on Recommender Systems. ACM, 49--56.

Digital Library

[25]

Bhaskar Mehta and Wolfgang Nejdl. 2008. Attack resistant collaborative filtering. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 75--82.

Digital Library

[26]

Bamshad Mobasher, Robin Burke, Runa Bhaumik, and Chad Williams. 2007. Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology (TOIT) 7, 4 (2007), 23.

Digital Library

[27]

Arash Molavi Kakhki, Chloe Kliman-Silver, and Alan Mislove. 2013. Iolaus: Securing online content rating systems. In Proceedings of the 22nd International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 919--930.

Digital Library

[28]

George L. Nemhauser, Laurence A. Wolsey, and Marshall L. Fisher. 1978. An analysis of approximations for maximizing submodular set functions. Mathematical Programming 14, 1 (1978), 265--294.

Digital Library

[29]

Michael O’Mahony, Neil Hurley, Nicholas Kushmerick, and Guénolé Silvestre. 2004. Collaborative recommendation: A robustness analysis. ACM Transactions on Internet Technology (TOIT) 4, 4 (2004), 344--377.

Digital Library

[30]

Myle Ott, Yejin Choi, Claire Cardie, and Jeffrey T. Hancock. 2011. Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Vol. 1, Association for Computational Linguistics, 309--319.

Digital Library

[31]

Shashank Pandit, Duen Horng Chau, Samuel Wang, and Christos Faloutsos. 2007. Netprobe: A fast and scalable system for fraud detection in online auction networks. In Proceedings of the 16th International Conference on World Wide Web. ACM, 201--210.

Digital Library

[32]

Bryan Perozzi, Leman Akoglu, Patricia Iglesias Sánchez, and Emmanuel Müller. 2014. Focused clustering and outlier detection in large attributed graphs. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1346--1355.

Digital Library

[33]

B. A. Prakash, M. Seshadri, A. Sridharan, S. Machiraju, and C. Faloutsos. 2010. Eigenspokes: Surprising patterns and community structure in large graphs. Pacific Asia Knowledge Discovery and Data Mining, 2010a. Vol. 84.

Digital Library

[34]

Anand Rajaraman, Jeffrey D. Ullman, Jeffrey David Ullman, and Jeffrey David Ullman. 2012. Mining of Massive Datasets. Vol. 1, Cambridge University Press Cambridge.

Digital Library

[35]

Neil Shah, Alex Beutel, Brian Gallagher, and Christos Faloutsos. 2014. Spotting suspicious link behavior with fBox: An adversarial perspective. In Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM’14). IEEE, 959--964.

Digital Library

[36]

Gianluca Stringhini, Manuel Egele, Christopher Kruegel, and Giovanni Vigna. 2012. Poultry markets: On the underground economy of twitter followers. In Proceedings of the 2012 ACM Workshop on Workshop on Online Social Networks. ACM, 1--6.

Digital Library

[37]

Gianluca Stringhini, Christopher Kruegel, and Giovanni Vigna. 2010. Detecting spammers on social networks. In Proceedings of the 26th Annual Computer Security Applications Conference. ACM, 1--9.

Digital Library

[38]

Steven H. Strogatz. 2001. Exploring complex networks. Nature 410, 6825 (2001), 268--276.

[39]

Dinh Nguyen Tran, Bonan Min, Jinyang Li, and Lakshminarayanan Subramanian. 2009. Sybil-resilient online content voting. In Proceedings of the 6th USENIX symposium on Networked Systems Design and Implementation, Vol. 9, 15--28.

Digital Library

[40]

Charalampos Tsourakakis. 2015. The K-clique densest subgraph problem. In Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 1122--1132.

Digital Library

[41]

Sankar Virdhagriswaran and Gordon Dakin. 2006. Camouflaged fraud detection in domains with complex relationships. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 941--947.

Digital Library

[42]

Hongning Wang, Yue Lu, and ChengXiang Zhai. 2011. Latent aspect rating analysis without aspect keyword supervision. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 618--626.

Digital Library

[43]

Steve Webb, James Caverlee, and Calton Pu. 2008. Social honeypots: Making friends with a spammer near you. In Conference Proceedings of on Email and Anti-Spam.

[44]

Baoning Wu, Vinay Goel, and Brian D. Davison. 2006. Propagating trust and distrust to demote web spam. In Proceedings of the Workshop on Models of Trust for the Web. Vol. 190.

[45]

Haifeng Yu, Phillip B. Gibbons, Michael Kaminsky, and Feng Xiao. 2008. Sybillimit: A near-optimal social network defense against sybil attacks. In Proceedings of the IEEE Symposium on Security and Privacy. IEEE, 3--17.

Digital Library

[46]

Haifeng Yu, Michael Kaminsky, Phillip B. Gibbons, and Abraham Flaxman. 2006. Sybilguard: Defending against sybil attacks via social networks. ACM SIGCOMM Computer Communication Review 36, 4 (2006), 267--278.

Digital Library

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Index Terms

Graph-Based Fraud Detection in the Face of Camouflage
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks
    2. Web searching and information discovery
      1. Content ranking

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 11, Issue 4

Special Issue on KDD 2016 and Regular Papers

November 2017

419 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3119906

Editor:
Jie Tang
Tsinghua University, China

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Copyright © 2017 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 June 2017

Accepted: 01 February 2017

Revised: 01 January 2017

Received: 01 November 2016

Published in TKDD Volume 11, Issue 4

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Cited By

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https://doi.org/10.1109/TSP.2025.3527755
Yu YShao MLi XWang W(2025)Temporal multivariate-factors independence convolution network for anomaly detection in dynamic networksNeurocomputing10.1016/j.neucom.2025.129439(129439)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2025.129439
Do TTa V(2025)Tackling under-reaching issue in Beta-Wavelet Filters with mixup augmentation for graph anomaly detectionExpert Systems with Applications10.1016/j.eswa.2025.127033(127033)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2025.127033
Chakraborty SSharov S(2025)Graph based approach on financial fraudulent detection and predictionApplied Graph Data Science10.1016/B978-0-443-29654-3.00019-3(25-37)Online publication date: 2025
https://doi.org/10.1016/B978-0-443-29654-3.00019-3
Xu FWang NWu HWen XZhao XWan HWooldridge MDy JNatarajan S(2024)Revisiting graph-based fraud detection in sight of heterophily and spectrumProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28773(9214-9222)Online publication date: 20-Feb-2024
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Ekle OEberle W(2024)Anomaly Detection in Dynamic Graphs: A Comprehensive SurveyACM Transactions on Knowledge Discovery from Data10.1145/366990618:8(1-44)Online publication date: 29-May-2024
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