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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%