Temporal transaction information-aware Ponzi scheme detection for ethereum smart contracts

Abstract

In recent years, the frenetic advances of blockchain techniques have promoted the large-scale application of cryptocurrency and attracted significant attention in the mushrooming applications of decentralized finance (DeFi). To guarantee the health of a DeFi ecosystem, it is critical to reduce the transaction risks in a DeFi system. In particular, as a representative DeFi ecosystem platform, Ethereum’s transaction process is mainly carried out with the help of smart contracts. Due to (pseudo)anonymity, the transaction process of Ethereum users is challenged by severe fraud threats. Ponzi scheme is the typical one. Previous studies have used machine learning methods to build Ponzi scheme detection models based on learning from the identified static smart contract samples feature data. However, in the early stage of smart contract deployment, the Ponzi scheme is difficult to detect. With the progress of transactions, Ponzi scheme will gradually show its characteristics. The existing methods are still falling short in capturing the temporal features of smart contracts for detecting Ponzi schemes in the big data environment. The recognition rate of the current approaches needs to be further improved. In this paper, we propose TTPS, a Long Short-Term Memory (LSTM) Ponzi scheme detection method considering time series transaction information of smart contracts. TTPS considers both temporal account features and code features of smart contracts. Adaptive synthetic sampling (ADASYN) is employed to effectively extend the feature data of minority class Ponzi scheme small samples. LSTM is utilized to learn from the temporal feature data of Ponzi scheme samples for TTPS model training. Experimental results verify and demonstrate the effectiveness and efficiency of TTPS.