ND-SYNC: Detecting synchronized fraud activities

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9078, Issue , 2015, Pages 201-214

  Giatsoglou, Maria ^a   Chatzakou, Despoina ^a   Shah, Neil ^b   Beutel, Alex ^b   Faloutsos, Christos ^b   Vakali, Athena ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b Carnegie Mellon University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; SOCIAL NETWORKING (ONLINE); SYNCHRONIZATION;

FRAUD DETECTION; FRAUDSTERS;

CRIME;

EID: 84945531564     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-18032-8_16     Document Type: Conference Paper

References (28)

1. Almaatouq, A., et al.: Twitter: who gets caught? observed trends in social microblogging spam. In: WebSci, pp. 33–41. ACM (2014)
2. Beutel, A., et al.: CopyCatch: stopping group attacks by spotting lockstep behavior in social networks. In: WWW, pp. 119–130. ACM (2013)
3. Breunig, M., et al.: LOF: identifying density-based local outliers. In: Proc. ACM SIGMOD Conf. 2000, pp. 93–104 (2000)
4. Brys, G., et al.: A Robust Measure of Skewness. Journal of Computational and Graphical Statistics 13, 996–1017 (2004)
5. Chan, P. K., et al.: Modeling multiple time series for anomaly detection. In: ICDM, pp. 90–97. IEEE Computer Society (2005)
6. Chandola, V., et al.: Anomaly Detection: A Survey. ACM Comput. Surv. 41(3), 15:1–15:58 (2009)
7. Chu, Z., et al.: Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? IEEE Trans. Dependable Secur. Comput. 9(6), 811–824 (2012)
8. Cook, D., et al.: Twitter Deception and Influence: Issues of Identity, Slacktivism, and Puppetry. Journal of Information Warfare 13(1), 58–71 (2014)
9. Freitas, C.A., et al.: Reverse Engineering Socialbot Infiltration Strategies in Twitter. ArXiv e-prints (2014)
10. Garrett, R.G.: The Chi-square Plot: a Tool for Multivariate Outlier Recognition. Journal of Geochemical Exploration 32, 319–341 (1989)
11. Ghosh, R., et al.: Entropy-based classification of ‘Retweeting’ activity on twitter. In: KDD Workshop on Social Network Analysis (SNA-KDD) (2011)
12. Ghoting, A., et al.: Fast mining of distance-based outliers in high-dimensional datasets. Data Mining and Knowledge Discovery 16(3), 349–364 (2008)
13. Hazel, G.: Multivariate Gaussian MRF for Multispectral Scene Segmentation and Anomaly Detection. IEEE Transactions on Geoscience and Remote Sensing 38(3), 1199–1211 (2000)
14. Hubert, M., et al.: ROBPCA: A New Approach to Robust Principal Component Analysis. Technometrics 47, 64–79 (2005)
15. Hubert, M., et al.: Robust PCA for Skewed Data and its Outlier Map. Computational Statistics & Data Analysis 53(6), 2264–2274 (2009)
16. Jiang, M., et al.: CatchSync: catching synchronized behavior in large directed graphs. In: KDD, pp. 941–950. ACM (2014)
17. Jiang, M., Cui, P., Beutel, A., Faloutsos, C., Yang, S.: Inferring strange behavior from connectivity pattern in social networks. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014, Part I. LNCS, vol. 8443, pp. 126–138. Springer, Heidelberg (2014)
18. Jolliffe, I.T.: Discarding Variables in a Principal Component Analysis. II: Real Data. Journal of the Royal Statistical Society. Series C (Applied Statistics) 22(1), 21–31 (1973)
19. Noble, C.C., et al.: Graph-based anomaly detection. In: KDD (2003)
20. Papadimitriou, S., et al.: LOCI: Fast outlier detection using the local correlation integral. In: ICDE 2003 (2003)
21. Shah, N., et al.: Spotting suspicious link behavior with fBox: an adversarial perspective. In: ICDM (2014)
22. Stringhini, G., et al.: Follow the green: growth and dynamics in twitter follower markets. In: IMC, pp. 163–176. ACM (2013)
23. Tavares, G., et al.: Scaling-Laws of Human Broadcast Communication Enable Distinction between Human, Corporate and Robot Twitter Users. PLoS ONE 8(7), e65774 (2013)
24. Twitter Inc. S-1 Filing, US Securities and Exchange Commission (2013). http://www.sec.gov/Archives/edgar/data/1418091/000119312513390321/d564001ds1.htm
25. Xiong, L., et al.: Group Anomaly Detection using Flexible Genre Models. Advances in Neural Information Processing Systems 24, 1071–1079 (2011)
26. Xiong, L., et al.: Efficient learning on point sets. In: ICDM, pp. 847–856 (2013)
27. Yang, C., et al.: Analyzing spammers’ social networks for fun and profit: a case study of cyber criminal ecosystem on twitter. In: WWW, pp. 71–80 (2012)
28. Yu, R., et al.: GLAD: group anomaly detection in social media analysis. In: KDD, pp. 372–381. ACM (2014)