Ponzi Contracts Detection Based on Improved Convolutional Neural Network

Abstract

As one of the leading blockchain systems in operation, Ethereum has numerous smart contracts deployed to implement a variety of functions. Unfortunately, speculators introduce scams such as Ponzi scheme in the traditional financial sector into some of these smart contracts, causing millions of dollars of losses to investors. At present, there are a few of quantitative identification methods for new fraud modes under the background of Internet finance, and detection methods for the Ponzi scheme contracts on Ethereum are even less. In this paper, we propose an improved convolutional neural network as a detection model for Ponzi schemes in smart contracts. We use real smart contracts to evaluate the feasibility and usefulness of our mode. Results show that our improved convolutional neural network can overcome difficulties in training caused by different length of smart contracts' bytecodes. Compared with the state-of-the-art methods, the precision and recall rate of our model for Ponzi scheme detection are improved by 3.2% and 24.8% respectively.