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Data-driven Smart Ponzi Scheme Detection

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arxiv 2108.09305 v1 pith:6XTYNUND submitted 2021-08-20 cs.LG cs.AIcs.CR

Yuzhi Liang , Weijing Wu , Kai Lei , Feiyang Wang This is my paper · ORCID
classification cs.LGcs.AIcs.CR
keywords ponzischemesmartdetectionaccountmethodssystemdata-driven
verification ladder T0 review T1 audit T2 compute T3 formal
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A smart Ponzi scheme is a new form of economic crime that uses Ethereum smart contract account and cryptocurrency to implement Ponzi scheme. The smart Ponzi scheme has harmed the interests of many investors, but researches on smart Ponzi scheme detection is still very limited. The existing smart Ponzi scheme detection methods have the problems of requiring many human resources in feature engineering and poor model portability. To solve these problems, we propose a data-driven smart Ponzi scheme detection system in this paper. The system uses dynamic graph embedding technology to automatically learn the representation of an account based on multi-source and multi-modal data related to account transactions. Compared with traditional methods, the proposed system requires very limited human-computer interaction. To the best of our knowledge, this is the first work to implement smart Ponzi scheme detection through dynamic graph embedding. Experimental results show that this method is significantly better than the existing smart Ponzi scheme detection methods.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PonziLens+: Visualizing Bytecode Actions for Smart Ponzi Scheme Identification

    cs.HC 2024-12 conditional novelty 6.0 of 10

    PonziLens+ extracts four semantic action types from Ethereum bytecode and visualizes them in three linked modules, enabling users to identify smart Ponzi schemes, including variants that avoid typical patterns.

  2. CASPER: Contrastive Approach for Smart Ponzi Scheme Detecter with More Negative Samples

    cs.CR 2025-07 reject novelty 4.0 of 10

    CASPER claims a triplet-view contrastive learning method with an equal-angle similarity vector improves smart Ponzi scheme detection over SourceP, especially with only 25% labels.

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