Table 10 The hyperparameters for encryption algorithms.
From: Enhancement of cryptography algorithms for security of cloud-based IoT with machine learning models
Model | The hyperparameter |
|---|---|
RF | n_estimators = 200, max_depth = 10, min_samples_split = 5, min_samples_leaf = 2, max_features=’sqrt’, and random_state = 42 |
XGboost | n_estimators = 200, learning_rate = 0.1, max_depth = 10, subsample = 0.8, colsample_bytree = 0.8, and objective= ‘binary: logistic’. |
CatBoost | iterations = 500, learning_rate = 0.1, depth = 10, l2_leaf_reg = 1, random_state = 42, and cat_features is [list of indices]. |
Voting ensemble | estimators is [(‘rf’, RF), (‘xgb’, XGBoost)], voting is ‘soft’, and weights are[0.5, 0.5]. |
Bagging classifier | n_estimators = 50, max_samples = 0.8, max_features = 0.8, random_state = 42. |
