A Fraud Detection Method for Low-Frequency Transaction
The effectiveness of transaction fraud detection methods directly affects the loss of users in online transactions. However, for low-frequency users with small transaction volume, the existing methods cannot accurately describe their transaction behaviors for each user, or lead to a high misjudgment...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8977544/ |
| _version_ | 1818917576038678528 |
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| author | Zhaohui Zhang Ligong Chen Qiuwen Liu Pengwei Wang |
| author_facet | Zhaohui Zhang Ligong Chen Qiuwen Liu Pengwei Wang |
| author_sort | Zhaohui Zhang |
| collection | DOAJ |
| description | The effectiveness of transaction fraud detection methods directly affects the loss of users in online transactions. However, for low-frequency users with small transaction volume, the existing methods cannot accurately describe their transaction behaviors for each user, or lead to a high misjudgment rate. So we propose a new method for individual behavior construction, which can make the behavior of low-frequency users more accurate by migrating the current transaction group behavior and transaction status. Firstly, we consider the user's only historical transactions, combined with the optimal risk threshold determination algorithm, to form the user's own transaction behavior benchmark. Secondly, through the DBSCAN clustering algorithm, the behavior characteristics of all current normal samples and fraud samples are extracted to form the common behavior of the current transaction group. Finally, based on historical transaction records, the current transaction status is extracted using a sliding window mechanism. The combination of the three constitutes a new transaction behavior of the user. On this basis, a multi-behavior detection model based on new transaction behavior is proposed. According to the result of each behavior, Naive Bayes model is used to calculate the probability that current transaction belongs to fraud, and finally determine whether current transaction is fraud. Experiments prove that the method proposed in this paper can have a good effect on low-frequency users, which can accurately identify fraud transactions and has a low misjudgment rate for normal transactions. |
| first_indexed | 2024-12-20T00:36:15Z |
| format | Article |
| id | doaj.art-fa5d0adb77d542c49886a82c7b01b1a6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T00:36:15Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fa5d0adb77d542c49886a82c7b01b1a62022-12-21T19:59:45ZengIEEEIEEE Access2169-35362020-01-018252102522010.1109/ACCESS.2020.29706148977544A Fraud Detection Method for Low-Frequency TransactionZhaohui Zhang0https://orcid.org/0000-0002-3171-7667Ligong Chen1https://orcid.org/0000-0003-3873-1557Qiuwen Liu2https://orcid.org/0000-0001-7064-7772Pengwei Wang3https://orcid.org/0000-0002-5667-3488School of Computer Science and Technology, Donghua University, Shanghai, ChinaSchool of Computer Science and Technology, Donghua University, Shanghai, ChinaSchool of Computer Science and Technology, Donghua University, Shanghai, ChinaSchool of Computer Science and Technology, Donghua University, Shanghai, ChinaThe effectiveness of transaction fraud detection methods directly affects the loss of users in online transactions. However, for low-frequency users with small transaction volume, the existing methods cannot accurately describe their transaction behaviors for each user, or lead to a high misjudgment rate. So we propose a new method for individual behavior construction, which can make the behavior of low-frequency users more accurate by migrating the current transaction group behavior and transaction status. Firstly, we consider the user's only historical transactions, combined with the optimal risk threshold determination algorithm, to form the user's own transaction behavior benchmark. Secondly, through the DBSCAN clustering algorithm, the behavior characteristics of all current normal samples and fraud samples are extracted to form the common behavior of the current transaction group. Finally, based on historical transaction records, the current transaction status is extracted using a sliding window mechanism. The combination of the three constitutes a new transaction behavior of the user. On this basis, a multi-behavior detection model based on new transaction behavior is proposed. According to the result of each behavior, Naive Bayes model is used to calculate the probability that current transaction belongs to fraud, and finally determine whether current transaction is fraud. Experiments prove that the method proposed in this paper can have a good effect on low-frequency users, which can accurately identify fraud transactions and has a low misjudgment rate for normal transactions.https://ieeexplore.ieee.org/document/8977544/Transaction detectionlow-frequency usersindividual behaviorgroup behaviorDBSCANNaive Bayes |
| spellingShingle | Zhaohui Zhang Ligong Chen Qiuwen Liu Pengwei Wang A Fraud Detection Method for Low-Frequency Transaction IEEE Access Transaction detection low-frequency users individual behavior group behavior DBSCAN Naive Bayes |
| title | A Fraud Detection Method for Low-Frequency Transaction |
| title_full | A Fraud Detection Method for Low-Frequency Transaction |
| title_fullStr | A Fraud Detection Method for Low-Frequency Transaction |
| title_full_unstemmed | A Fraud Detection Method for Low-Frequency Transaction |
| title_short | A Fraud Detection Method for Low-Frequency Transaction |
| title_sort | fraud detection method for low frequency transaction |
| topic | Transaction detection low-frequency users individual behavior group behavior DBSCAN Naive Bayes |
| url | https://ieeexplore.ieee.org/document/8977544/ |
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