Table 2 Related work detecting Ethereum Ponzi schemes

Chen et al. (2021a)

Semantically-aware classifier that includes “a heuristic-guided symbolic execution technique”

Code-based

Precision: 100%

Recall: 100%

F1: 100%

Fan et al. (2021)

“Anti-leakage” model based on ordered boosting

Code-based

Precision: 95%

Recall: 96%

F1: 96%

Hu and Xu (2021)

Deep learning model

Code-based

Precision: 96.3%

Recall: 97.8%

F1: 97.1%

Hu et al. (2021)

Long-term short-term memory neural network

Transaction-based

Precision: Between 88.2% and 96.9% for different types of contracts

Recall: Between 81.6% and 97.7% for different types of contracts

F1: Between 85% and 96.7% for different types of contracts

Wang et al. (2021)

Long-term short-term memory neural network

Code- and transaction-based

Precision: 97%

Recall: 96%

F1: 96%

Liu et al. (2022)

Heterogeneous Graph Transformer Networks

Code- and transaction-based

F1: Between 78% and 82% for fraudulent smart contracts and 87% and 89% for normal smart contracts for different classification tasks

Zhang et al. (2021)

LightGBM

Code- and transaction-based

Precision: 96.7%

Recall: 96.7%

F1: 96.7%

Chen et al. (2018)

XGBoost

Code- and transaction-based

Precision: 94%

Recall: 81%

F1: 86%

Jung et al. (2019)

Decision trees, random forest, stochastic gradient descent

Code- and transaction-based

Precision: Between 90% and 98% for different models

Recall: Between 80% and 96% for different models

F1: Between 84% and 96% for different models

Chen et al. (2019)

Random forest

Code- and transaction-based

Precision: Between 64% and 95% for different features

Recall: Between 20% and 73% for different features

F1: Between 30% and 82% for different features