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5. V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection: A survey. CSUR , 41(3):1-
58, 2009.
6. J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters.
CACM , 51(1):107-113, 2008.
7. M. Freedman, M. Vutukuru, N. Feamster, and H. Balakrishnan. Geographic locality of
IP prefixes. In SIGCOMM IMC , pp. 13-13, 2005.
8. J. Friedman. Multivariate adaptive regression splines. Annals of Statistics , 19(1), 1991.
9. B. Grow, B. Elgin, and M. Herbst. Click fraud—The dark side of online advertising.
Business Week Online , 10(02), 2006.
10. J. Hamilton. Time series analysis . Princeton University Press, illustrated edition, 1994.
11. I. T. Jolliffe. Principal component analysis , volume 487. Springer-Verlag, New York,
1986.
12. A. Kind, M. Stoecklin, and X. Dimitropoulos. Histogram-based traffic anomaly detec-
tion. IEEE Transactions on Network and Service Management , 6(2):110-121, 2010.
13. K. Kira and L. Rendell. The feature selection problem: Traditional methods and a new
algorithm. In AAAI , pages 129-129. John Wiley & Sons Ltd., 1992.
14. N. Kshetri. The economics of click fraud. IEEE Security and Privacy , 8(3):45-53, 2010.
15. J. Lin. Divergence measures based on the shannon entropy. IEEE Transactions on
Information Theory , 37(1):145-151, 1991.
16. A. Metwally, D. Agrawal, and A. El Abbadi. DETECTIVES: DETEcting Coalition hiT
Inflation attacks in adVertising nEtworks Streams. In WWW , pp. 241-250, 2007.
17. A. Metwally, D. Agrawal, A. El Abbadi, and Q. Zheng. On hit inflation techniques and
detection in streams of web advertising networks. In ICDCS , 2007.
18. A. Metwally, F. Emekçi, D. Agrawal, and A. El Abbadi. SLEUTH: Single-pubLisher
attack dEtection Using correlaTion Hunting. Proceedings of the VLDB Endowment ,
1(2):1217-1228, 2008.
19. A. Metwally and M. Paduano. Estimating the number of users behind ip addresses for
combating abusive traffic. In SIGKDD , pp. 249-257. ACM, 2011.
20. R. Pike, S. Dorward, R. Griesemer, and S. Quinlan. Interpreting the data: Parallel analy-
sis with Sawzall. Scientific Programming , 13(4):277-298, 2005.
21. G. Snedecor and W. Cochran. Statistical Methods . John Wiley & Sons, 8th edition,
1991.
22. F. Soldo and A. Metwally. Traffic anomaly detection based on the ip size distribution. In
INFOCOM , pp. 2005-2013. IEEE, 2012.
23. P. Sollich and A. Krogh. Learning with ensembles: How over-fitting can be useful.
Advances in neural information processing systems , pp. 190-196, 1996.
24. Spamhaus XBL. http://www.spamhaus.org/xbl/.
25. B. Taylor. Sender reputation in a large webmail service. In CEAS , 2006.
26. C. Tofallis. Least squares percentage regression. Journal of Modern Applied Statistical
Methods , 2009.
27. Y. Xie, F. Yu, K. Achan, E. Gillum, M. Goldszmidt, and T. Wobber. How dynamic are IP
addresses? In SIGCOMM , pp. 301-312, 2007.
28. L. Zhuang, J. Dunagan, D. Simon, H. Wang, and J. Tygar. Characterizing botnets from
email spam records. In LEET , pp. 1-9, 2008.
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