A Scalable Temporal-Spatial Framework for Transaction Anomaly Detection in Ethereum Networks

Stefan Behfar, Jon Crowcroft

Keywords: Probabilistic sampling, Temporal random walk, Graph convolutional network, Transaction anomaly detection, Ethereum networks

TL;DR: TRW-GCN a scalable framework for transaction anomaly detection

Abstract: The rapid evolution of the Ethereum network necessitates sophisticated techniques to ensure its robustness against potential threats and to maintain transparency. While Graph Neural Networks (GNNs) have pioneered anomaly detection in such platforms, capturing the intricacies of both spatial and temporal transactional patterns has remained a challenge. This study presents a fusion of Graph Convolutional Networks (GCNs) with Temporal Random Walks (TRW) enhanced by probabilistic sampling to bridge this gap. Our approach, unlike traditional GCNs, leverages the strengths of TRW to discern complex temporal sequences in Ethereum transactions, thereby providing a more nuanced transaction anomaly detection mechanism. Extensive evaluations demonstrate that our TRW-GCN framework substantially advances the performance metrics over conventional GCNs in detecting irregularities such as suspiciously timed transactions, patterns indicative of token pump and dump schemes, or anomalous behavior in smart contract executions over time. As baseline algorithms for comparison, common unsupervised methods such as Isolation Forest, One-Class SVM, and DBSCAN (as classifier for TRW-GCN embedding) are employed; finally our novel TRW-GCN plus scoring method is compared with the state-of-the-art temporal graph attention algorithm.

Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)

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Submission Number: 6429