Abstract
Click Farming is fraudulent behaviors sponsored by malicious merchants to increase exposure by hiring fraudulent teams to place fraudulent orders, posing a serious threat to the operation of platforms. Traditional anti-fraud strategies are no longer applicable as they analyzed fraudulent behaviors individually and only rely on static statistical characteristics. In this paper, we propose a novel graph-based fraud detection framework deployed on JD.com composed of Dynamic Purchase Pattern learning (DPP) and Graph Neural Network with Similarities and Relations (GSR). Specifically, the DPP module is a feature extractor based on user click location sequences collected from websites. And the GSR module is a neighborhood sampling and aggregation algorithm for locating more accurate fraud groups and aggregating various information encoded by different types of subgroups. We conduct graph node classification experiments on a large-scale real-world dataset to verify the effectiveness of our framework, and the experimental results show that the DPP is able to capture more discriminative user patterns. Furthermore, GSR achieves the best performance compared to several state-of-the-art methods. Our method can be easily extended to other domains with the same problems as our task.
S. Wang and Y. Liu—Contribute equally to this work.
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References
Bahnsen, A.C., Aouada, D., Stojanovic, A., Ottersten, B.: Feature engineering strategies for credit card fraud detection. Expert Syst. Appl. 51, 134–142 (2016)
Barkan, O., Koenigstein, N.: ITEM2VEC: neural item embedding for collaborative filtering. In: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6. IEEE (2016)
Bhattacharyya, S., Jha, S., Tharakunnel, K., Westland, J.C.: Data mining for credit card fraud: a comparative study. Decis. Support Syst. 50(3), 602–613 (2011)
Bickart, B., Schindler, R.M.: Internet forums as influential sources of consumer information. J. Interact. Mark. 15(3), 31–40 (2001)
Chandradeva, L.S., Amarasinghe, T.M., De Silva, M., Aponso, A.C., Krishnarajah, N.: Monetary transaction fraud detection system based on machine learning strategies. In: Yang, X.-S., Sherratt, S., Dey, N., Joshi, A. (eds.) Fourth International Congress on Information and Communication Technology. AISC, vol. 1041, pp. 385–396. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0637-6_33
Eberle, W., Holder, L.: Discovering structural anomalies in graph-based data. In: Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), pp. 393–398. IEEE (2007)
Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., Ghosh, R.: Exploiting burstiness in reviews for review spammer detection. In: Seventh International AAAI Conference on Weblogs and Social Media (2013)
Georgieva, S., Markova, M., Pavlov, V.: Using neural network for credit card fraud detection. In: AIP Conference Proceedings, vol. 2159, p. 030013. AIP Publishing LLC (2019)
Glover, S., Benbasat, I.: A comprehensive model of perceived risk of e-commerce transactions. Int. J. Electron. Commer. 15(2), 47–78 (2010)
Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024–1034 (2017)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Coference on Learning Representations (ICLR) (2017)
Lim, E.P., Nguyen, V.A., Jindal, N., Liu, B., Lauw, H.W.: Detecting product review spammers using rating behaviors. In: Proceedings of the 19th ACM International Conference on Information And Knowledge Management, pp. 939–948 (2010)
Manzoor, E., Milajerdi, S.M., Akoglu, L.: Fast memory-efficient anomaly detection in streaming heterogeneous graphs. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1035–1044 (2016)
Pourhabibi, T., Ong, K.L., Kam, B.H., Boo, Y.L.: Fraud detection: a systematic literature review of graph-based anomaly detection approaches. Decis. Support Syst. 133, 113303 (2020)
Shehnepoor, S., Salehi, M., Farahbakhsh, R., Crespi, N.: Netspam: a network-based spam detection framework for reviews in online social media. IEEE Trans. Inf. Forensics Secur. 12(7), 1585–1595 (2017)
Sun, C., Gu, H., Hu, J.: Scalable and adaptive graph neural networks with self-label-enhanced training. arXiv preprint arXiv:2104.09376 (2021)
Tao, J., Wang, H., Xiong, T.: Selective graph attention networks for account takeover detection. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 49–54. IEEE (2018)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (ICLR) (2018)
Wang, S., Liu, C., Gao, X., Qu, H., Xu, W.: Session-based fraud detection in online e-commerce transactions using recurrent neural networks. In: Altun, Y., et al. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10536, pp. 241–252. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71273-4_20
Wu, X., Dong, Y., Tao, J., Huang, C., Chawla, N.V.: Reliable fake review detection via modeling temporal and behavioral patterns. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 494–499. IEEE (2017)
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: International Conference on Learning Representations (ICLR) (2019)
Zhang, C., He, Y., Cen, Y., Hou, Z., Tang, J.: Improving the training of graph neural networks with consistency regularization. arXiv preprint arXiv:2112.04319 (2021)
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Wang, S., Liu, Y., Zheng, C., Lin, R. (2022). Purchase Pattern Based Anti-Fraud Framework in Online E-Commerce Platform Using Graph Neural Network. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13605. Springer, Cham. https://doi.org/10.1007/978-3-031-20500-2_9
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