An Evaluation of Variational Autoencoder in Credit Card Anomaly Detection
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| Publicado en: | Big Data Mining and Analytics vol. 7, no. 3 (Sep 2024), p. 718 |
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Tsinghua University Press
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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|---|---|---|---|
| 001 | 3202838652 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2096-0654 | ||
| 024 | 7 | |a 10.26599/BDMA.2023.9020035 |2 doi | |
| 035 | |a 3202838652 | ||
| 045 | 2 | |b d20240901 |b d20240930 | |
| 100 | 1 | |a Alshameri, Faleh | |
| 245 | 1 | |a An Evaluation of Variational Autoencoder in Credit Card Anomaly Detection | |
| 260 | |b Tsinghua University Press |c Sep 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Anomaly detection is one of the many challenging areas in cybersecurity. The anomaly can occur in many forms, such as fraudulent credit card transactions, network intrusions, and anomalous imageries or documents. One of the most common challenges in anomaly detection is the obscurity of the normal state and the lack of anomalous samples. Traditionally, this problem is tackled by using resampling techniques or choosing models that approximate the distribution of the normal states. Variational AutoEncoder (VAE) has been studied in anomaly detections despite being more suitable in generative tasks. This study aims to explore the usage of VAE in credit card anomaly detection and evaluate latent space sampling techniques. In this study, we evaluate the usage of the convolutional network-based VAE model on a credit card transaction dataset. We train two VAE models, one with a large number of normal data and one with a small number of anomalous data. We compare the performance of both VAE models and evaluate the latent space of both VAE models by rescaling them with reconstruction error vectors. We also compare the effectiveness of the VAE model with other anomaly detection models when they are trained on imbalanced dataset. | |
| 653 | |a Datasets | ||
| 653 | |a Anomalies | ||
| 653 | |a Rescaling | ||
| 653 | |a Resampling | ||
| 653 | |a Sampling methods | ||
| 653 | |a Credit cards | ||
| 653 | |a Cybersecurity | ||
| 653 | |a Random variables | ||
| 653 | |a Fraud prevention | ||
| 653 | |a Neural networks | ||
| 653 | |a Electrocardiography | ||
| 653 | |a Approximation | ||
| 653 | |a Data analysis | ||
| 653 | |a Literature reviews | ||
| 653 | |a Time series | ||
| 653 | |a Performance evaluation | ||
| 700 | 1 | |a Xia, Ran | |
| 773 | 0 | |t Big Data Mining and Analytics |g vol. 7, no. 3 (Sep 2024), p. 718 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3202838652/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3202838652/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |