Graph Deep Learning Based Anomaly Detection in Ethereum Blockchain Network

Ethereum is one of the largest blockchain networks in the world. Its feature of smart contracts is unique among the other crypto-currencies and gained wider attention. However, smart contracts are vulnerable to attacks and financial fraud within the network. Identifying anomalies in this massive network is challenging because of anonymity. Using traditional machine learning-based techniques, such as One-Class Support Vector Machine and Isolation Forest are ineffective in Identifying anomalies in the Ethereum transactions because of its limitations in terms of capturing the internode or account relationship information in the transactions. Ethereum transactions can be effectively represented using an attributed graph with nodes and edges capturing the inter-dependencies. Hence, in this paper, we propose to use a One-Class Graph Neural Network-based anomaly detection framework for detecting anomalies in the Ethereum blockchain network. Empirical evaluation demonstrates that the proposed method is able to achieve higher anomaly detection accuracy than traditional non-graph based machine learning algorithms.