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User click fraud detection method based on Top-Rank-k frequent pattern mining

Lijiao Pan

Yiwu Industrial & Commercial College, Yiwu 322000, P. R. China

E-mail Address: Augustine301@protonmail.com

Corresponding author.

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Shibiao Mu

Yiwu Industrial & Commercial College, Yiwu 322000, P. R. China

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, and

Yingyan Wang

Yiwu Industrial & Commercial College, Yiwu 322000, P. R. China

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https://doi.org/10.1142/S0217979219501509Cited by:8 (Source: Crossref)

Abstract

A user click fraud detection method based on Top-Rank-k frequent pattern mining algorithm is presented to solve the click fraud problem appearing in current online advertising. Firstly, this method combines the click frequency of event samples, calculates the real evaluation score of click stream, and the click stream density function and evaluation score expression under multi-dimensional variables, and further obtains the time complexity of the next user’s click fraud process. Secondly, according to the Top-Rank-k frequent pattern, the process of click fraud detection algorithm is designed, and the click fraud user is analyzed and obtained. The results show that this method has good efficiency and correctness, and is superior to other similar algorithms.

Keywords:

PACS: 05.10.-a, 07.05.mh

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References

1. P. Ravisankar et al., Decis. Support Syst. 50(2), 273 (2011). Crossref, Web of Science, Google Scholar
2. J. A. Gómez et al., Pattern Recogni. Lett. 105, 75 (2018). Crossref, Web of Science, ADS, Google Scholar
3. D. Berrar, Knowl. Inf. Syst. 46(2), 477 (2016). Crossref, Web of Science, Google Scholar
4. X. Zhu et al., Stat. Interface 9, 389 (2016). Crossref, Web of Science, Google Scholar
5. B. Kitts et al., Click fraud detection with bot signatures, in IEEE Int. Conf. Intelligence and Security Informatics (2013), pp. 146–150. Crossref, Google Scholar
6. R. Oentaryo et al., J. Mach. Learn. Res. 15(1), 99 (2014). Google Scholar
7. X. Li et al., IEEE Intell. Syst. 26(5), 31 (2011). Crossref, Web of Science, Google Scholar
8. M. Kantardzic, Improving click fraud detection by real time data fusion, in IEEE Int. Symp. Signal Processing and Information Technology (2008), pp. 69–74. Crossref, Google Scholar
9. Q. Lu and C. Ju, J. Converg. Inf. Technol. 6(1), 62 (2011). Google Scholar
10. B. R. Bakshi, AICh E J. 44(7), 1596 (1998). Crossref, Web of Science, ADS, Google Scholar
11. A. Bakdi and A. Kouadri, Chemometri. Intell. Lab. Syst. 162(1), 83 (2017). Crossref, Web of Science, Google Scholar
12. Q. Wen, Z. Ge and Z. Song, AICh E J. 58(12), 3763 (2012). Crossref, Web of Science, ADS, Google Scholar
13. J. Shang et al., Automatica 80(1), 313 (2017). Crossref, Web of Science, Google Scholar
14. E. J. Candes et al., J. ACM 58(3), 11:1 (2011). Crossref, Web of Science, Google Scholar
15. V. Zoran et al., Physica A 511, 331 (2018). Crossref, Web of Science, Google Scholar
16. S. Eugen et al., Decis. Support Syst. 111, 13 (2018). Crossref, Web of Science, Google Scholar
17. C. Nuno, F. Gonçalo and C. Miguel, Decis. Support Syst. 95, 91 (2017). Crossref, Web of Science, Google Scholar
18. S. Chandra et al., Decis. Support Syst. 74, 78 (2015). Crossref, Web of Science, Google Scholar
19. H. Haddadi, ACM SIGCOMM Comput. Commun. Rev. 40(2), 21 (2010). Crossref, Google Scholar
20. R. Oentaryo et al., J. Mach. Learn. Res. 14(1), 99 (2014). Google Scholar
21. C. C. Chaang and C. J. Lin, ACM Trans. Intell. Technol. 2(3), 389 (2011). Google Scholar
22. S. J. Peter and S. P. Victor, J. Discret. Math. Sci. Cryptogr. 14(14), 89 (2011). Crossref, Google Scholar
23. W. N. Robinson and A. Aria, Expert Syst. Appl. 91, 235 (2018). Crossref, Web of Science, Google Scholar
24. M. del Mar Roldán-García, J. García-Nieto and J. F. Aldana-Montes, Expert Syst. Appl. 90, 332 (2017). Crossref, Web of Science, Google Scholar
25. W. Chamila, K. Mehmed and W. Brent, Int. J. Data Anal. Tech. Strateg. 4(4), 340 (2012). Crossref, Google Scholar
26. K. S. Perera et al., A novel ensemble learning-based approach for click fraud detection in mobile advertising, in Mining Intelligence and Knowledge Exploration (Springer International Publishing, Switzerland, 2013), pp. 370–382. Crossref, Google Scholar
27. H. Chen and C. Michael, Ann. Rev. Inf. Sci. Technol. 38(1), 289 (2005). Crossref, Web of Science, Google Scholar