CATS: Cross-platform e-commerce fraud detection

Haiqin Weng, Shouling Ji, Fuzheng Duan, Zhao Li, Jianhai Chen, Qinming He, Ting Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nowadays, the popularity of e-commerce has brought huge economic benefits to factories, third-party merchants, and e-commerce service providers. Driven by such huge economic benefits, malicious merchants attempt to promote items through inserting fraudulent purchases, fake review scores, and/or feedback, into them. Mitigating this threat is challenging due to the difficulty of obtaining internal e-commerce data, the variance of e-commerce services used by malicious merchants, and the reluctance of service providers in cooperation. In this paper, we present an efficient, platform-independent, and robust e-commerce fraud detection system, CATS, to detect frauds for different large-scale e-commerce platforms. We implement the design of CATS into a prototype system and evaluate this prototype on the world's popular e-commerce platform Taobao. The evaluation result on Taobao shows that CATS can achieve a high accuracy of 91% in detecting frauds. Based on this success, we then apply CATS on another large-scale e-commerce platforms, and again CATS achieves an accuracy of 96%, suggesting that CATS is very effective on real e-commerce platforms. Based on the cross-platform evaluation results, we conduct a comprehensive analysis on the reported frauds and reveal several abnormal yet interesting behaviors of those reported frauds. Our study in this paper is expected to shed light on defending against frauds for various e-commerce platforms.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PublisherIEEE Computer Society
Pages1874-1885
Number of pages12
ISBN (Electronic)9781538674741
DOIs
StatePublished - Apr 1 2019
Event35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China
Duration: Apr 8 2019Apr 11 2019

Publication series

NameProceedings - International Conference on Data Engineering
Volume2019-April
ISSN (Print)1084-4627

Conference

Conference35th IEEE International Conference on Data Engineering, ICDE 2019
CountryChina
CityMacau
Period4/8/194/11/19

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All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

Cite this

Weng, H., Ji, S., Duan, F., Li, Z., Chen, J., He, Q., & Wang, T. (2019). CATS: Cross-platform e-commerce fraud detection. In Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019 (pp. 1874-1885). [8731582] (Proceedings - International Conference on Data Engineering; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/ICDE.2019.00203
Weng, Haiqin ; Ji, Shouling ; Duan, Fuzheng ; Li, Zhao ; Chen, Jianhai ; He, Qinming ; Wang, Ting. / CATS : Cross-platform e-commerce fraud detection. Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society, 2019. pp. 1874-1885 (Proceedings - International Conference on Data Engineering).
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title = "CATS: Cross-platform e-commerce fraud detection",
abstract = "Nowadays, the popularity of e-commerce has brought huge economic benefits to factories, third-party merchants, and e-commerce service providers. Driven by such huge economic benefits, malicious merchants attempt to promote items through inserting fraudulent purchases, fake review scores, and/or feedback, into them. Mitigating this threat is challenging due to the difficulty of obtaining internal e-commerce data, the variance of e-commerce services used by malicious merchants, and the reluctance of service providers in cooperation. In this paper, we present an efficient, platform-independent, and robust e-commerce fraud detection system, CATS, to detect frauds for different large-scale e-commerce platforms. We implement the design of CATS into a prototype system and evaluate this prototype on the world's popular e-commerce platform Taobao. The evaluation result on Taobao shows that CATS can achieve a high accuracy of 91{\%} in detecting frauds. Based on this success, we then apply CATS on another large-scale e-commerce platforms, and again CATS achieves an accuracy of 96{\%}, suggesting that CATS is very effective on real e-commerce platforms. Based on the cross-platform evaluation results, we conduct a comprehensive analysis on the reported frauds and reveal several abnormal yet interesting behaviors of those reported frauds. Our study in this paper is expected to shed light on defending against frauds for various e-commerce platforms.",
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Weng, H, Ji, S, Duan, F, Li, Z, Chen, J, He, Q & Wang, T 2019, CATS: Cross-platform e-commerce fraud detection. in Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019., 8731582, Proceedings - International Conference on Data Engineering, vol. 2019-April, IEEE Computer Society, pp. 1874-1885, 35th IEEE International Conference on Data Engineering, ICDE 2019, Macau, China, 4/8/19. https://doi.org/10.1109/ICDE.2019.00203

CATS : Cross-platform e-commerce fraud detection. / Weng, Haiqin; Ji, Shouling; Duan, Fuzheng; Li, Zhao; Chen, Jianhai; He, Qinming; Wang, Ting.

Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society, 2019. p. 1874-1885 8731582 (Proceedings - International Conference on Data Engineering; Vol. 2019-April).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Weng H, Ji S, Duan F, Li Z, Chen J, He Q et al. CATS: Cross-platform e-commerce fraud detection. In Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society. 2019. p. 1874-1885. 8731582. (Proceedings - International Conference on Data Engineering). https://doi.org/10.1109/ICDE.2019.00203