Data-driven Smart Ponzi Scheme Detection
This addresses the detection of a new form of economic crime harming cryptocurrency investors, offering an incremental improvement in automation and accuracy.
The paper tackles the problem of detecting smart Ponzi schemes on Ethereum by proposing a data-driven system that uses dynamic graph embedding to automatically learn account representations from transaction data, reducing human effort and achieving significantly better performance than existing methods.
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.