1 |
BEHDAD M, BARONE L, BENNAMOUN M, et al. Nature-inspired techniques in the context of fraud detection. IEEE Transactions on Systems, Man, and Cybernetics, 2012, 42 (6): 1273- 1290.
doi: 10.1109/TSMCC.2012.2215851
|
2 |
ALPAYDN G. An adaptive deep neural network for detection, recognition of objects with long range auto surveillance[C]//Proceedings of the 12th IEEE International Conference on Semantic Computing. Washington D. C., USA: IEEE Press, 2018: 316-317.
|
3 |
YANG J, ZHOU C J, YANG S H, et al. Anomaly detection based on zone partition for security protection of industrial cyber-physical systems. IEEE Transactions on Industrial Electronics, 2018, 65 (5): 4257- 4267.
doi: 10.1109/TIE.2017.2772190
|
4 |
KARAMI A. An anomaly-based intrusion detection system in presence of benign outliers with visualization capabilities. Expert Systems with Applications, 2018, 108, 36- 60.
doi: 10.1016/j.eswa.2018.04.038
|
5 |
KODAMA T, KAMATA K, FUJIWARA K, et al. Ischemic stroke detection by analyzing heart rate variability in rat middle cerebral artery occlusion model. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2018, 26 (6): 1152- 1160.
doi: 10.1109/TNSRE.2018.2834554
|
6 |
刘华玲, 刘雅欣, 许珺怡, 等. 图异常检测在金融反欺诈中的应用研究进展. 计算机工程与应用, 2022, 58 (22): 41- 53.
URL
|
|
LIU H L, LIU Y X, XU J Y, et al. Research progress on the application of anomaly detection in financial anti-fraud. Computer Engineering and Applications, 2022, 58 (22): 41- 53.
URL
|
7 |
陈卓, 朱淼, 杜军威. 基于多视角图神经网络的欺诈检测算法. 通信学报, 2022, 43 (11): 225- 232.
URL
|
|
CHEN Z, ZHU M, DU J W. Fraud detection algorithm based on multi-view graph neural network. Journal on Communications, 2022, 43 (11): 225- 232.
URL
|
8 |
POURHABIBI T, ONG K L, KAM B H, et al. Fraud detection: a systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 2020, 133, 113303.
doi: 10.1016/j.dss.2020.113303
|
9 |
AL-ZOUBI A M, FARIS H, ALQATAWNA J, et al. Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts. Knowledge-Based Systems, 2018, 153, 91- 104.
doi: 10.1016/j.knosys.2018.04.025
|
10 |
PRADO-ROMERO M A, OLIVA A F, HERNÁNDEZ L G. Identifying twitter users influence and open mindedness using anomaly detection. Berlin, Germany: Springer, 2018: 166- 173.
|
11 |
RAMALINGAM D, CHINNAIAH V. Fake profile detection techniques in large-scale online social networks: a comprehensive review. Computers & Electrical Engineering, 2018, 65, 165- 177.
|
12 |
AL-QURISHI M, HOSSAIN M S, ALRUBAIAN M, et al. Leveraging analysis of user behavior to identify malicious activities in large-scale social networks. IEEE Transactions on Industrial Informatics, 2018, 14 (2): 799- 813.
doi: 10.1109/TII.2017.2753202
|
13 |
DOU Y T, LIU Z W, SUN L, et al. Enhancing graph neural network-based fraud detectors against camouflaged fraudsters[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management. New York, USA: ACM Press, 2020: 315-324.
|
14 |
LIU Z W, DOU Y T, YU P S, et al. Alleviating the inconsistency problem of applying graph neural network to fraud detection[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 2020: 1569-1572.
|
15 |
RAYANA S, AKOGLU L. Collective opinion spam detection: bridging review networks and metadata[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2015: 985-994.
|
16 |
LIU Y, AO X, QIN Z D, et al. Pick and choose: a GNN-based imbalanced learning approach for fraud detection[C]//Proceedings of Web Conference. New York, USA: ACM Press, 2021: 3168-3177.
|
17 |
ZHANG G, WU J, YANG J, et al. FRAUDRE: fraud detection dual-resistant to graph inconsistency and imbalance[C]//Proceedings of IEEE International Conference on Data Mining. Washington D. C., USA: IEEE Press, 2022: 867-876.
|
18 |
WANG X, ZHU M Q, BO D Y, et al. AM-GCN: adaptive multi-channel graph convolutional networks[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: ACM Press, 2020: 1243-1253.
|
19 |
代黎. 基于代价敏感的不平衡分类问题实证研究[D]. 武汉: 华中师范大学, 2019.
|
|
DAI L. Empirical research on unbalanced classification based on cost sensitivity[D]. Wuhan: Central China Normal University, 2019. (in Chinese)
|
20 |
MCAULEY J J, LESKOVEC J. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews[C]//Proceedings of the 22nd International Conference on World Wide Web. New York, USA: ACM Press, 2013: 897-908.
|
21 |
HAMILTON W L, YING R, LESKOVEC J. Inductive representation learning on large graphs[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York, USA: ACM Press, 2017: 1025-1035.
|
22 |
|
23 |
|
24 |
LIU Z Q, CHEN C C, YANG X X, et al. Heterogeneous graph neural networks for malicious account detection[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York, USA: ACM Press, 2018: 2077-2085.
|
25 |
WANG J Y, WEN R, WU C M, et al. FdGars: fraudster detection via graph convolutional networks in online App review system[C]//Proceedings of 2019 World Wide Web Conference. New York, USA: ACM Press, 2019: 310-316.
|
26 |
ZHANG Y M, FAN Y J, YE Y F, et al. Key player identification in underground forums over attributed heterogeneous information network embedding framework[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York, USA: ACM Press, 2019: 549-558.
|