Anomaly Detection with Autoencoder and Random Forest
| Title | Anomaly Detection with Autoencoder and Random Forest |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Lin, T.-H., Jiang, J.-R. |
| Conference Name | 2020 International Computer Symposium (ICS) |
| Date Published | Dec. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-9255-0 |
| Keywords | anomaly detection, autoencoder, Computational modeling, Credit cards, Deep Learning, feature extraction, ICS Anomaly Detection, pubcrawl, Radio frequency, Random Forest, random forests, resilience, Resiliency, Scalability, Tuning |
| Abstract | This paper proposes AERFAD, an anomaly detection method based on the autoencoder and the random forest, for solving the credit card fraud detection problem. The proposed AERFAD first utilizes the autoencoder to reduce the dimensionality of data and then uses the random forest to classify data as anomalous or normal. Large numbers of credit card transaction data of European cardholders are applied to AEFRAD to detect possible frauds for the sake of performance evaluation. When compared with related methods, AERFAD has relatively excellent performance in terms of the accuracy, true positive rate, true negative rate, and Matthews correlation coefficient. |
| URL | https://ieeexplore.ieee.org/document/9359048 |
| DOI | 10.1109/ICS51289.2020.00028 |
| Citation Key | lin_anomaly_2020 |