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Table 4 Public blockchain service behavior risk awareness work comparison

From: Blockchain abnormal behavior awareness methods: a survey

Service behavior

Awareness difficulty

Awareness methods

Technical details

Applicable scene

Awareness accuracy

Awareness speed

Advantage

Disadvantage

Precision

Recall

F1

Data using

Ponzi schemes

Easy

Based on supervised learning

Using XGBoost to classify the Ethereum transaction flow graphs (Chen et al. 2018)

Blockchain with sufficient transaction scale

High

Fast

High precision

Unsatisfactory recall rate

94.00%

81.00%

86.00%

Ethereum transaction data

Based on mixed information model construction

Fusion of transaction records, contract source code and on chain information to build a Ponzi scheme behavior model (Bartolettiet al. 2020)

Blockchain with sufficient transaction volume and support for smart contracts

–

–

Comprehensive analysis dimensions

Multi-dimensional data is weakly correlated

Find extra 184 ponzi schemes on Ethereum

Based on unsupervised learning

Smart contract operation code and account feature extraction based on deep neural network (Zhang and Lou 2021)

All blockchains that support smart contracts

High

Fast

High precision and recall rates

High data set dependence

99.60%

96.30%

97.92%

Ethereum transaction data combined with contract code data

Money laundering

Easy

Based on unsupervised learning

Compare the transaction flow graph between the normal transactions and money laundering transactions (Hu et al. 2019)

Blockchain with sufficient transaction scale

High

Medium

High precision

Unable to identify blockchain application with insufficient transaction scale

92.74%

97.37%

95.00%

Bitcoin partial transaction datasets

Based on supervised learning

Use graph neural network to identify and visualize money laundering transaction sequence diagrams (Weber et al. 2019)

Popular crypto-currency platform

High

Medium

High robustness and suitable for all kinds of popular crypto-currency platform

Unsatisfactory accuracy

97.10%

67.50%

79.00%

Based on active learning

Use less data to achieve recognition (Lorenz et al. 2020)

Blockchain with limited data and manpower

Medium

Medium

Independent of data set size

Robustness needs to be verified

With the number of labeled samples, the best F1: 83.00%