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Table 2 Balanced accuracy, Precision, and Recall, and F1 score for both malicious (Mal) and benign (Ben) accounts with best identified ML algorithm for supervised case when using different dataset configurations

From: Detecting malicious accounts in permissionless blockchains using temporal graph properties

Features Data segment Best classifier Accuracy balanced Precision Recall F1 score MCC score
Mal Ben Mal Ben Mal Ben
28 Only EOA ExtraTrees 0.872 0.38 1.00 0.75 0.99 0.50 1.00 0.510
(PCA) EOA and SC 0.873 0.22 1.00 0.76 0.99 0.34 0.99 0.421
36 Only EOA 0.876 0.11 1.00 0.78 0.97 0.19 0.99 0.254
EOA and SC 0.882 0.24 1.00 0.78 0.99 0.37 0.99 0.429
59 Only EOA 0.881 0.26 1.00 0.77 0.99 0.38 0.99 0.425
EOA and SC 0.887 0.29 1.00 0.78 0.99 0.42 1.00 0.473
  1. Here, we also report the Matthews Correlation Coefficient (MCC) score
  2. 28 (PCA) EOA ExtraTreesClassifier(class_weight = ‘balanced’, max_features = 0.4, max_samples = 0.3, min_samples_leaf = 11, min_samples_split = 19, n_estimators = 600)
  3. 28 (PCA) EOA and SC ExtraTreesClassifier(class_weight = ‘balanced’, criterion = ’entropy’, max_features = 0.25, max_samples = 0.15, min_samples_leaf = 13, min_samples_split = 4, n_estimators = 800, n_jobs = 20, random_state = 100)
  4. 36 EOA ExtraTreesClassifier(bootstrap = true, class_weight = ‘balanced’, max_features = 0.15, max_samples = 0.7, min_samples_leaf = 8, min_samples_split = 18, n_estimators = 200, n_jobs = 10, random_state = 100)
  5. 36 EOA and SC ExtraTreesClassifier(class_weight = ‘balanced’, criterion = ’entropy’, max_features = 0.45, max_samples = 0.75, min_samples_leaf = 18, min_samples_split = 6, n_estimators = 200)
  6. 59 EOA ExtraTreesClassifier(class_weight = ‘balanced’, max_features = 0.2, max_samples = 0.75, min_samples_leaf = 13, min_samples_split = 19)
  7. 59 EOA and SC ExtraTreesClassifier(class_weight = ‘balanced’, criterion = ’entropy’, max_features = 0.3, max_samples = 0.3, min_samples_leaf = 14, min_samples_split = 20, n_estimators = 200)