The DOI matches its Crossref record; 134 of the 134 references it cites were found in a registry or an index.
Fraud detection system: A survey
Overview
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Citations
Cites
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Data mining: concepts and techniques
(2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,531
(2014) · Climate Change 2013 – The Physical Science Basis · doi:10.1017/cbo9781107415324.004 · Cited by 23,880
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
A survey on concept drift adaptation
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014) · ACM Computing Surveys · doi:10.1145/2523813 · Cited by 3,735
Exploratory Undersampling for Class-Imbalance Learning
Liu, X.-Y., Wu, J., & Zhou, Z.-H. (2009) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2008.2007853 · Cited by 2,465
Resnick, P., Kuwabara, K., Zeckhauser, R., & Friedman, E. (2000) · Communications of the ACM · doi:10.1145/355112.355122 · Cited by 2,190 · cited 1×
“Mean-while, feedback reputation system contains rating scale from 1 to 5, or using several measures (friendliness, prompt response, quality product, etc.), or averaging rather than totalling feedback scores (Resnick et al., 2000).”
BackgroundBleiholder, J., & Naumann, F. (2009) · ACM Computing Surveys · doi:10.1145/1456650.1456651 · Cited by 1,838
Introduction to Semi-Supervised Learning
Zhu, X., & Goldberg, A. B. (2009) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.1007/978-3-031-01548-9 · Cited by 1,766
Statistical Fraud Detection: A Review
Bolton, R. J., & Hand, D. J. (2002) · Statistical Science · doi:10.1214/ss/1042727940 · Cited by 1,450
Cited by
A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects
Mienye, I. D., & Sun, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3207287 · Cited by 1,298
Deep Learning for Anomaly Detection: A Survey
Chalapathy, R., & Chawla, S. (2019) · arXiv · doi:10.48550/arxiv.1901.03407 · Cited by 1,240
A Unifying Review of Deep and Shallow Anomaly Detection
Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3052449 · Cited by 914
Digital Twins: State of the art theory and practice, challenges, and open research questions
Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100383 · Cited by 587
Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612
“Attack monitoring has been widely adopted in conventional SaaS, such as fraud detection [3] in e-commerce systems.”
Method
Metadata
- Authors
- Abdallah, A., Maarof, M. A., & Zainal, A.
- Venue
- Journal of Network and Computer Applications
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 68
- Pages
- 90-113
- ISSN
- 1084-8045 (ISSN-L)
- OpenAlex
- W2533835508
- Cited by
- 708
- References
- 134
- FWCI
- 32.09
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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How to cite
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- This page
- CoChat. (2026). Citation check: Fraud detection system: A survey. https://app.cochat.ai/papers/10.1016/j.jnca.2016.04.007
- The paper
- Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2016.04.007
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
