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A method for online transaction fraud detection based on individual behavior

Published: 17 May 2019 Publication History

Abstract

Nowadays, judging the current transaction based on user history transactions is an important detection method. However, different users have different transaction behaviors, when all users use the same limit to judge whether the transaction is abnormal, it will result in higher misjudgment for some users. Aiming at the above problems, this paper proposes an individual behavior transaction detection method based on hypersphere model. In this model, considering multiple dimensions of normal historical transaction records, the characteristics of user's transaction behavior is generated with the trend of transaction. Then, the user optimal risk threshold algorithm is proposed to determine the optimal risk threshold for each user. Finally combining the transaction behavior and the optimal risk threshold, the user behavior benchmark is formed, which is used to construct the multidimensional hypersphere model. On this basis, a mapping method for transforming transaction detection into midpoint in multidimensional space is proposed. The experiment proves that the proposed method is superior to other models, and it is found that the characterization effect of user behavior is related to the frequency of users' transactions.

References

[1]
Samaneh Sorournejad, Zahra Zojaji, Reza Ebrahimi Atani, and Amir Hassan Monadjemi. 2016. A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective. (11 2016).
[2]
Rong Chang Chen, Shu Ting Luo, Liang Xun, and V. C. S. Lee. 2005. Personalized Approach Based on SVM and ANN for Detecting Credit Card Fraud. In 2005 International Conference on Neural Networks and Brain, Vol. 2. 810--815.
[3]
A. C. Bahnsen, A. Stojanovic, D. Aouada, and B. Ottersten. 2013. Cost Sensitive Credit Card Fraud Detection Using Bayes Minimum Risk. In 2013 12th International Conference on Machine Learning and Applications, Vol. 1. 333--338.
[4]
J. I. Bing-Shuai, L. I. Hu, Wei Hong Han, and Yan Jia. 2014. Research on E-commerce-oriented User Abnormal Behaviour Detection. Netinfo Security (2014).
[5]
R. Brause, T. Langsdorf, and M. Hepp. 1999. Neural data mining for credit card fraud detection. In Proceedings 11th International Conference on Tools with Artificial Intelligence. 103--106.
[6]
Chyxx 2018. Forecast of the market size of China's online shopping industry in 2018. Chyxx. http://www.chyxx.com/industry/201803/614936.html.
[7]
Andrea Dal Pozzolo, Olivier Caelen, Yann A ël Le Borgne, Serge Waterschoot, and Gianluca Bontempi. 2014. Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications 41 (08 2014), 4915--4928.
[8]
Liu Haiou. 2018. Literature Review of Persona at Home and Abroad. Information Studies:Theory Application (2018).
[9]
C. Jiang, J. Song, G. Liu, L. Zheng, and W. Luan. 2018. Credit Card Fraud Detection: A Novel Approach Using Aggregation Strategy and Feedback Mechanism. IEEE Internet of Things Journal 5, 5 (Oct 2018), 3637--3647.
[10]
Morteza Kolali Khormuji, Mehrnoosh Bazrafkan, Maryam Sharifian, Seyed Mirabedini, and Ali Harounabadi. 2014. Credit Card Fraud Detection with a Cascade Artificial Neural Network and Imperialist Competitive Algorithm. International Journal of Computer Applications 96 (06 2014), 1--9.
[11]
Yigit Kultur and Mehmet Ufuk Caglayan. 2015. A novel cardholder behavior model for detecting credit card fraud. In 2015 9th International Conference on Application of Information and Communication Technologies (AICT). 148--152.
[12]
Dominik Olszewski. 2014. Fraud detection using self-organizing map visualizing the user profiles. Knowledge-Based Systems 70 (11 2014), 324--334.
[13]
A. Shen, R. Tong, and Y. Deng. 2007. Application of Classification Models on Credit Card Fraud Detection. In 2007 International Conference on Service Systems and Service Management. 1--4.
[14]
Souhu 2018. Research report on the trend of Network Fraud in 2017. Souhu. https://www.sohu.com/a/222391501_100017648.
[15]
Dheepa V and Dhanapal R. 2012. Behavior based credit card fraud detection using support vector machines. ICTACT Journal on Soft Computing 02 (07 2012), 391--397.
[16]
S. Wang. 2010. A Comprehensive Survey of Data Mining-Based Accounting Fraud Detection Research. In 2010 International Conference on Intelligent Computation Technology and Automation, Vol. 1. 50--53.
[17]
C. Whitrow, D. J. Hand, P. Juszczak, D. Weston, and N. M. Adams. 2009. Transaction aggregation as a strategy for credit card fraud detection. Data Mining and Knowledge Discovery 18, 1 (01 Feb 2009), 30--55.
[18]
Ws 2018. big data observation report on double 11 in 2018. Ws. http://www.100ec.cn/detail-6481169.html.
[19]
S. Xuan, G. Liu, Z. Li, L. Zheng, S. Wang, and C. Jiang. 2018. Random forest for credit card fraud detection. In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). 1--6.
[20]
Masoumeh Zareapoor and Pourya Shamsolmoali. 2015. Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier. Procedia Computer Science 48 (12 2015), 679--686.
[21]
Zhaohui Zhang, Xinxin Zhou, Xiaobo Zhang, Lizhi Wang, and Pengwei Wang. 2018. A Model Based on Convolutional Neural Network for Online Transaction Fraud Detection. Security and Communication Networks 2018 (08 2018), 1--9.
[22]
Z.-H Zhang and J Cui. 2017. An Agile Perception Method for Behavior Abnormality in Large-Scale Network Service Systems. Jisuanji Xuebao/Chinese Journal of Computers 40 (02 2017), 505--519.
[23]
L. Zheng, G. Liu, W. Luan, Z. Li, Y. Zhang, C. Yan, and C. Jiang. 2018. A new credit card fraud detecting method based on behavior certificate. In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC). 1--6.
[24]
J. Zhong, C. Yan, W. Yu, P. Zhao, and M. Wang. 2014. A Kind of Identity Authentication Method Based on Browsing Behaviors. In 2014 Seventh International Symposium on Computational Intelligence and Design, Vol. 2. 279--284.

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cover image ACM Other conferences
ACM TURC '19: Proceedings of the ACM Turing Celebration Conference - China
May 2019
963 pages
ISBN:9781450371582
DOI:10.1145/3321408
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 17 May 2019

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Author Tags

  1. behavior benchmark
  2. hypersphere model
  3. individual behavior
  4. online transaction detection
  5. optimal risk threshold
  6. transaction behavior

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Cited By

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  • (2024)UBRMTC: User Behavior Recognition Model With Transaction CharacterIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.325722711:2(1589-1601)Online publication date: Apr-2024
  • (2024)Event-Aware Multi-component (EMl) Loss for Fraud DetectionPattern Recognition10.1007/978-3-031-78398-2_7(105-119)Online publication date: 2-Dec-2024
  • (2023)Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.331949225:4(2245-2298)Online publication date: Dec-2024
  • (2023)How can we learn from a borrower’s online behaviors? The signal effect of a borrower’s platform involvement on its credit riskElectronic Commerce Research and Applications10.1016/j.elerap.2023.10127259(101272)Online publication date: May-2023
  • (2022)Rule-Based Credit Card Fraud Detection Using User’s Keystroke BehaviorSoft Computing: Theories and Applications10.1007/978-981-19-0707-4_43(469-480)Online publication date: 2-Jun-2022
  • (2021)Privacy Intrusiveness in Financial-Banking Fraud DetectionRisks10.3390/risks90601049:6(104)Online publication date: 1-Jun-2021
  • (2021)A Fraud Detection System Using Machine Learning2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT51525.2021.9580102(1-7)Online publication date: 6-Jul-2021
  • (2021)A survey for user behavior analysis based on machine learning techniques: current models and applicationsApplied Intelligence10.1007/s10489-020-02160-x51:8(6029-6055)Online publication date: 26-Jan-2021
  • (2020)Evaluation of Deep Neural Networks for Reduction of Credit Card Fraud AlertsIEEE Access10.1109/ACCESS.2020.30262228(186421-186432)Online publication date: 2020
  • (2020)A Fraud Detection Method for Low-Frequency TransactionIEEE Access10.1109/ACCESS.2020.29706148(25210-25220)Online publication date: 2020

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