Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection

• URL: http://arxiv.org/abs/2506.21382v1
• Date: Thu, 26 Jun 2025 15:34:06 GMT
• Title: Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection
• Authors: Zhi Zheng, Bochuan Zhou, Yuping Song,
• Abstract summary: This paper proposes an Augmented Temporal-aware Graph Attention Network (ATGAT) that enhances detection performance.<n>Experiments on the Elliptic++ cryptocurrency dataset demonstrate that ATGAT achieves an AUC of 0.9130, representing a 9.2% improvement over the best traditional method.
• Score: 2.1161810694525807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cryptocurrency transaction fraud detection faces the dual challenges of increasingly complex transaction patterns and severe class imbalance. Traditional methods rely on manual feature engineering and struggle to capture temporal and structural dependencies in transaction networks. This paper proposes an Augmented Temporal-aware Graph Attention Network (ATGAT) that enhances detection performance through three modules: (1) designing an advanced temporal embedding module that fuses multi-scale time difference features with periodic position encoding; (2) constructing a temporal-aware triple attention mechanism that jointly optimizes structural, temporal, and global context attention; (3) employing weighted BCE loss to address class imbalance. Experiments on the Elliptic++ cryptocurrency dataset demonstrate that ATGAT achieves an AUC of 0.9130, representing a 9.2% improvement over the best traditional method XGBoost, 12.0% over GCN, and 10.0% over standard GAT. This method not only validates the enhancement effect of temporal awareness and triple attention mechanisms on graph neural networks, but also provides financial institutions with more reliable fraud detection tools, with its design principles generalizable to other temporal graph anomaly detection tasks.

Related papers

• Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention [2.7002727600755883]
  This study proposes a credit card fraud detection method based on Heterogeneous Graph Neural Network (HGNN)<n>By leveraging graph neural networks, the model captures higher-order transaction relationships.<n>The model achieves notable improvements in both accuracy and OC-ROC.
  arXiv  Detail & Related papers  (2025-04-11T00:53:53Z)
• Heterogeneous Graph Auto-Encoder for CreditCard Fraud Detection [0.7864304771129751]
  This paper proposes a novel approach for credit card fraud detection using Graph Neural Networks (GNNs) with attention mechanisms applied to heterogeneous graph representations of financial data. The proposed model outperforms benchmark algorithms such as Graph Sage and FI-GRL, achieving a superior AUC-PR of 0.89 and an F1-score of 0.81.
  arXiv  Detail & Related papers  (2024-10-10T17:05:27Z)
• Explainable AI for Fraud Detection: An Attention-Based Ensemble of CNNs, GNNs, and A Confidence-Driven Gating Mechanism [5.486205584465161]
  This study presents a new stacking-based approach for CCF detection by adding two extra layers to the usual classification process.<n>In the attention layer, we combine soft outputs from a convolutional neural network (CNN) and a recurrent neural network (RNN) using the dependent ordered weighted averaging (DOWA) operator.<n>In the confidence-based layer, we select whichever aggregate (DOWA or IOWA) shows lower uncertainty to feed into a meta-learner.<n>Experiments on three datasets show that our method achieves high accuracy and robust generalization, making it effective for CCF detection.
  arXiv  Detail & Related papers  (2024-10-01T09:56:23Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality [39.476378833827184]
  Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. We introduce a novel spatial- temporal memories-enhanced graph autoencoder (STRIPE) STRIPE significantly outperforms existing methods with 5.8% improvement in AUC scores and 4.62X faster in training time.
  arXiv  Detail & Related papers  (2024-03-14T02:26:10Z)
• Probabilistic Sampling-Enhanced Temporal-Spatial GCN: A Scalable Framework for Transaction Anomaly Detection in Ethereum Networks [2.795656498870966]
  This study presents a fusion of Graph Convolutional Networks (GCNs) with Temporal Random Walks (TRW) Our approach, unlike traditional GCNs, leverages the strengths of TRW to discern complex temporal sequences in transactions. Preliminary evaluations demonstrate that our TRW-GCN framework substantially advances performance metrics over conventional GCNs.
  arXiv  Detail & Related papers  (2023-09-29T21:08:21Z)
• Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer [5.043422340181098]
  We propose a novel graph neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for transaction fraud detection problems. Specifically, we design a temporal encoding strategy to capture temporal dependencies and incorporate it into the graph neural network framework. We introduce a transformer module to learn local and global information.
  arXiv  Detail & Related papers  (2023-07-11T08:56:53Z)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models [53.67562579184457]
  This paper focuses on probabilistic STG forecasting, which is challenging due to the difficulty in modeling uncertainties and complex dependencies. We present the first attempt to generalize the popular denoising diffusion models to STGs, leading to a novel non-autoregressive framework called DiffSTG. Our approach combines the intrinsic-temporal learning capabilities STNNs with the uncertainty measurements of diffusion models.
  arXiv  Detail & Related papers  (2023-01-31T13:42:36Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction [77.57991021445959]
  We propose a neural layer based on the ideas of temporal attention and multi-head attention to extend the capability of the underlying neural network. The effectiveness of our approach is validated using large-scale limit-order book market data.
  arXiv  Detail & Related papers  (2022-01-14T14:02:19Z)
• Distribution-sensitive Information Retention for Accurate Binary Neural Network [49.971345958676196]
  We present a novel Distribution-sensitive Information Retention Network (DIR-Net) to retain the information of the forward activations and backward gradients. Our DIR-Net consistently outperforms the SOTA binarization approaches under mainstream and compact architectures. We conduct our DIR-Net on real-world resource-limited devices which achieves 11.1 times storage saving and 5.4 times speedup.
  arXiv  Detail & Related papers  (2021-09-25T10:59:39Z)
• Semantic Perturbations with Normalizing Flows for Improved Generalization [62.998818375912506]
  We show that perturbations in the latent space can be used to define fully unsupervised data augmentations. We find that our latent adversarial perturbations adaptive to the classifier throughout its training are most effective.
  arXiv  Detail & Related papers  (2021-08-18T03:20:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.