Table 1 Features used in related studies
From: Detecting malicious accounts in permissionless blockchains using temporal graph properties
# | B/C | Used features based on (See Abbreviation section) | ML Algo used | Dataset | Hyperparameters | Performance | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
AS | iD | oD | Bal | TF | BB | A | CC | IET | ||||||
Pham and Lee (2016) | B | ✓ | ✓ | ✓ | ✓ | – | – | – | ✓ | ✓ | K-means | 100\(\hbox {K}^a\) | \(k \in [1,14]\) | \(k_{opt}=7,8\) |
Mahalanobis distance | \(\times\) | 0.0256\(^{MDE}\) | ||||||||||||
\(\nu\)-SVM | \(\nu =0.005\) | 0.1441\(^{MDE}\) | ||||||||||||
Pham and Lee (2017) | B | ✓ | ✓ | ✓ | ✓ | – | - | – | – | ✓ | Local outlier factor | 6.3\(\hbox {M}^a\) | \(k = 8\) | 0.55\(^{MDE}\) |
Monamo et al. (2016) | B | – | ✓ | ✓ | ✓ | – | – | – | ✓ | – | K-means | 1\(\hbox {M}^a\) | \(k \in [1,14]\) | \(k_{opt}=8\) |
Trimmed K-means | \(k \in [1,15]\), \(\alpha =0.01\) | \(k_{opt}=8\) | ||||||||||||
Bartoletti et al. (2018) | B | ✓ | ✓ | ✓ | ✓ | – | – | – | – | ✓ | RIPPER\(\dagger\) | \(\ddagger\)6432\(^a\) | Cost \(\in [1,40]\) | 0.996\(^{ac}\) |
Bayes network | \(\times\) | 0.983\(^{ac}\) | ||||||||||||
Random Forest | \(\times\) | 0.996\(^{ac}\) | ||||||||||||
Chen et al. (2018b) | E | – | ✓ | ✓ | ✓ | ✓ | – | – | – | – | XGBoost | \(\ddagger\)1382\(^{sc}\) | \(\times\) | 0.94\(^p\), 0.81\(^r\) |
Ostapowicz and Zbikowski (2019) | E | ✓ | ✓ | ✓ | – | ✓ | – | – | – | – | Random Forest | 350\(\hbox {K}^a\) | RFPARAM | 0.85\(^{r}\), 0.05\(^{p}\) |
SVM | \(Cost=1\), \(\gamma =0.077\) | 0.87\(^{r}\), 0.02\(^{p}\) | ||||||||||||
XGBoost | XGBPARAM | 0.8\(^{r}\), 0.07\(^{p}\) | ||||||||||||
Singh (2019) | E | ✓ | ✓ | ✓ | ✓ | – | – | – | – | – | Decision Tree | 300\(^a\) | \(\times\) | 0.93\(^{ac}\) |
SVM | \(\times\) | 0.83\(^{ac}\) | ||||||||||||
KNN | \(k=5\) | 0.91\(^{ac}\) | ||||||||||||
MLP | \(\times\) | 0.86\(^{ac}\) | ||||||||||||
NaiveBayes | \(\times\) | 0.89\(^{ac}\) | ||||||||||||
Random Forest | \(\times\) | 0.99\(^{ac}\) | ||||||||||||
Kumar et al. (2020) | E | ✓ | – | – | ✓ | ✓ | – | – | – | – | Decision Tree | 9375\(^a\) | \(\times\) | 0.92\(^{ac}\) |
KNN | \(\times\) | 0.92\(^{ac}\) | ||||||||||||
XGBoost | \(\times\) | 0.96\(^{ac}\) | ||||||||||||
Random Forest | \(\times\) | 0.95\(^{ac}\) | ||||||||||||
Zola et al. (2019) | B | – | ✓ | ✓ | ✓ | ✓ | – | – | – | – | Adaboost | 1000\(\hbox {M}^a\) | \(Estimators=50\), \(rate=1\) | \(>0.2^{r}\) |
Random Forest | \(Estimators=10\) | \(>0.85^{r}\) | ||||||||||||
Gradient boosting | \(estimators=100\), \(rate=0.1\) | \(>0.93^{r}\) | ||||||||||||
\(Depth=3\) | ||||||||||||||