Behavior-aware Account De-anonymization on Ethereum Interaction Graph

Abstract

Blockchain technology has the characteristics of decentralization, traceability and tamper-proof, which creates a reliable decentralized trust mechanism, further accelerating the development of blockchain finance. However, the anonymization of blockchain hinders market regulation, resulting in increasing illegal activities such as money laundering, gambling and phishing fraud on blockchain financial platforms. Thus, financial security has become a top priority in the blockchain ecosystem, calling for effective market regulation. In this paper, we consider identifying Ethereum accounts from a graph classification perspective, and propose an end-to-end graph neural network framework named Ethident, to characterize the behavior patterns of accounts and further achieve account de-anonymization. Specifically, we first construct an Account Interaction Graph (AIG) using raw Ethereum data. Then we design a hierarchical graph attention encoder named HGATE as the backbone of our framework, which can effectively characterize the node-level account features and subgraph-level behavior patterns. For alleviating account label scarcity, we further introduce contrastive self-supervision mechanism as regularization to jointly train our framework. Comprehensive experiments on Ethereum datasets demonstrate that our framework achieves superior performance in account identification, yielding 1.13% ~ 4.93% relative improvement over previous state-of-the-art. Furthermore, detailed analyses illustrate the effectiveness of Ethident in identifying and understanding the behavior of known participants in Ethereum (e.g. exchanges, miners, etc.), as well as that of the lawbreakers (e.g. phishing scammers, hackers, etc.), which may aid in risk assessment and market regulation.

arXiv commentAccepted by IEEE Transactions on Information Forensics & Security

References in corpus (10)

• Fast unfolding of communities in large networks
• DeepWalk: Online Learning of Social Representations
• struc2vec: Learning Node Representations from Structural Identity
• graph2vec: Learning Distributed Representations of Graphs
• Applications of Blockchain Technology beyond Cryptocurrency
• The Unreasonable Effectiveness of Address Clustering
• Subgraph Networks with Application to Structural Feature Space Expansion
• Identity Inference on Blockchain using Graph Neural Network
• Tracking bitcoin users activity using community detection on a network of weak signals
• Data Augmentation for Graph Classification

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• Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum
• Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation
• Clarify Confused Nodes via Separated Learning
• Multi-view Correlation-aware Network Traffic Detection on Flow Hypergraph