Probabilistic Temporal Sampling for Anomaly Detection in Ethereum Networks

Stefan Behfar, Richard Mortier, Jon Crowcroft

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

TL;DR: TRW-GCN for Anomaly Detection in Ethereum Networks

Abstract: The rapid growth of the Ethereum network necessitates advanced anomaly detection techniques to enhance security, transparency, and resilience against evolving malicious activities. While there have been significant strides in anomaly detection, they often fall short in capturing the intricate spatial-temporal patterns inherent in blockchain transactional data. This study presents a scalable framework that integrates Graph Convolutional Networks (GCNs) with Temporal Random Walks (TRW) specifically designed to adapt to the complexities and temporal dynamics of the Ethereum transaction network. Unlike traditional methods that focus on detecting specific attack types, such as front-running or flash loan exploits, our approach targets time-sensitive anomalies more broadly—detecting irregularities such as rapid transaction bursts, anomalous token swaps, and sudden volume spikes. This broader focus reduces reliance on pre-defined attack categories, making the method more adaptable to emerging and evolving malicious strategies. To ground our contributions, we establish three theoretical results: (1) the effectiveness of TRW in enhancing GCN-based anomaly detection by capturing temporal dependencies, (2) the identification of weight cancellation conditions in the anomaly detection process, and (3) the scalability and efficiency improvements of GCNs achieved through probabilistic sampling. Empirical evaluations demonstrate that the TRW-GCN framework outperforms state-of-the-art Temporal Graph Attention Networks (TGAT) in detecting time-sensitive anomalies. Furthermore, as part of our ablation study, we evaluated various anomaly detection techniques on the TRW-GCN embeddings and found that our proposed scoring classifier consistently achieves higher accuracy and precision compared to baseline methods such as Isolation Forest, One-Class SVM, and DBSCAN, thereby validating the robustness and adaptability of our framework.

Primary Area: Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences)

Submission Number: 20843