Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network

Jiang, Shanshan; Dong, Ruiting; Wang, Jie; Xia, Min

doi:10.3390/systems11060305

Cited by 18 publications

(7 citation statements)

References 46 publications

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“…In this context, f n×n (•) represents a convolutional layer with a kernel size of n, followed by batch normalization and the ReLU activation function [52]. abs(•) indicates performing the absolute difference operation.…”

Section: Difference Feature Module (Dfm)mentioning

confidence: 99%

MDANet: A High-Resolution City Change Detection Network Based on Difference and Attention Mechanisms under Multi-Scale Feature Fusion

Jiang,

Lin,

Ren

et al. 2024

Remote Sensing

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In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection systems to deal with. Target misdetection, missed detections, and edge blurring are further problems with current deep learning-based methods. This research proposes a high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion (MDANet) to address these issues and improve the accuracy of change detection. First, to extract features from dual-temporal remote sensing pictures, we use the Siamese architecture as the encoder network. The Difference Feature Module (DFM) is employed to learn the difference information between the dual-temporal remote sensing images. Second, the extracted difference features are optimized with the Attention Refinement Module (ARM). The Cross-Scale Fusion Module (CSFM) combines and enhances the optimized attention features, effectively capturing subtle differences in remote sensing images and learning the finer details of change targets. Finally, thorough tests on the BTCDD dataset, LEVIR-CD dataset, and CDD dataset show that the MDANet algorithm performs at a cutting-edge level.

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Section: Difference Feature Module (Dfm)mentioning

confidence: 99%

MDANet: A High-Resolution City Change Detection Network Based on Difference and Attention Mechanisms under Multi-Scale Feature Fusion

Jiang,

Lin,

Ren

et al. 2024

Remote Sensing

Self Cite

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“…In this study, the class distribution was investigated and validated on imbalanced target data distribution. In [20], a novel Unsupervised-Attentional-Anomaly-Detection-Network-based CCF classification model (UAAD-FDNet) was introduced. Various transactions occur in a day while fraudulent transactions among them are considered as abnormal instances.…”

Section: Related Workmentioning

confidence: 99%

Sustainable Financial Fraud Detection Using Garra Rufa Fish Optimization Algorithm with Ensemble Deep Learning

Maashi,

Alabduallah,

Kouki

2023

Sustainability

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Sustainable financial fraud detection (FD) comprises the use of sustainable and ethical practices in the detection of fraudulent activities in the financial sector. Credit card fraud (CCF) has dramatically increased with the advances in communication technology and e-commerce systems. Recently, deep learning (DL) and machine learning (ML) algorithms have been employed in CCF detection due to their features’ capability of building a powerful tool to find fraudulent transactions. With this motivation, this article focuses on designing an intelligent credit card fraud detection and classification system using the Garra Rufa Fish optimization algorithm with an ensemble-learning (CCFDC-GRFOEL) model. The CCFDC-GRFOEL model determines the presence of fraudulent and non-fraudulent credit card transactions via feature subset selection and an ensemble-learning process. To achieve this, the presented CCFDC-GRFOEL method derives a new GRFO-based feature subset selection (GRFO-FSS) approach for selecting a set of features. An ensemble-learning process, comprising an extreme learning machine (ELM), bidirectional long short-term memory (BiLSTM), and autoencoder (AE), is used for the detection of fraud transactions. Finally, the pelican optimization algorithm (POA) is used for parameter tuning of the three classifiers. The design of the GRFO-based feature selection and POA-based hyperparameter tuning of the ensemble models demonstrates the novelty of the work. The simulation results of the CCFDC-GRFOEL technique are tested on the credit card transaction dataset from the Kaggle repository and the results demonstrate the superiority of the CCFDC-GRFOEL technique over other existing approaches.

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“…Jiang et al [ 26 ] introduced a novel unsupervised attentional anomaly detection network-based framework for credit card fraud detection. Their model combines a generator and a discriminator: the former incorporates an autoencoder, while the latter contributes to an adversarial training setup.…”

Section: Related Studiesmentioning

confidence: 99%

Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression

Chung,

Lee

2023

Sensors

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Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as “IF” and “THEN”, and operators, such as “>“ and “<“, to the results. The features extracted using this proposed strategy achieved a recall of 1.0000, 0.9701, 1.0000, and 0.9362 across the four tested fraud datasets. Consequently, this methodology outperforms other approaches employing single machine learning models in terms of recall.

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Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network

Cited by 18 publications

References 46 publications

MDANet: A High-Resolution City Change Detection Network Based on Difference and Attention Mechanisms under Multi-Scale Feature Fusion

MDANet: A High-Resolution City Change Detection Network Based on Difference and Attention Mechanisms under Multi-Scale Feature Fusion

Sustainable Financial Fraud Detection Using Garra Rufa Fish Optimization Algorithm with Ensemble Deep Learning

Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression

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