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.
Data-driven Smart Ponzi Scheme Detection
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abstract
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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CASPER: Contrastive Approach for Smart Ponzi Scheme Detecter with More Negative Samples
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.