Heterogeneous Feature Augmentation for Ponzi Detection in Ethereum

IEEE Transactions on Circuits and Systems II: Express Briefs(2022)

Cited 5|Views8
No score
Abstract
While blockchain technology triggers new industrial and technological revolutions, it also brings new challenges. Recently, a large number of new scams with a “blockchain” sock-puppet continue to emerge, such as Ponzi schemes, money laundering, etc., seriously threatening financial security. Existing fraud detection methods in blockchain mainly concentrate on manual features and graph analytics, which first construct a homogeneous transaction graph using partial blockchain data and then use graph analytics to detect anomaly, resulting in a loss of pattern information. In this brief, we mainly focus on Ponzi scheme detection and propose HFAug , a generic Heterogeneous Feature Augmentation module that can capture the heterogeneous information associated with account behavior patterns and can be combined with existing Ponzi detection methods. HFAug learns the metapath-based behavior characteristics in an auxiliary heterogeneous interaction graph, and aggregates the heterogeneous features to corresponding account nodes in the homogeneous one where the Ponzi detection methods are performed. Comprehensive experimental results demonstrate that our HFAug can help existing Ponzi detection methods achieve significant performance improvement on Ethereum datasets, suggesting the effectiveness of heterogeneous information on detecting Ponzi schemes.
More
Translated text
Key words
Ethereum,Ponzi scheme detection,heterogeneous graph,metapath
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined