Intrusion Detection in SCADA Based Power Grids: Recursive Feature Elimination Model with Majority Vote Ensemble Algorithm

Keyphrases

• Accurate Solution 33%
• Adaboost Tree 33%
• Artificial Neural Network 33%
• Bagging Ensemble 33%
• Boosting Ensemble 33%
• Decision Tree 33%
• Detection Accuracy 33%
• Efficient Detection 33%
• Ensemble Algorithm 100%
• Ensemble Methods 66%
• Extra Trees 33%
• Extreme Gradient Boosting 33%
• F1 Score 33%
• Feature Importance Score 33%
• Feature Selection 33%
• Gradient Boosting Decision Tree 33%
• Heterogeneous Classifier 66%
• Intrusion Detection 100%
• Intrusion Detection System 33%
• K-nearest 33%
• Label-only 33%
• Majority Vote 100%
• Miss Rate 33%
• Multi-class 33%
• Naïve Bayes 33%
• Power Grid 100%
• Precision-recall 66%
• Random Forest 33%
• Receiver Operating Characteristic 33%
• Recursive Feature Elimination 100%
• Selected Features 33%
• Supervisory Control 100%
• Three-class 33%
• Training Process 33%
• Weighted Features 33%
• XGBoost 33%

Computer Science

• Artificial Neural Network 50%
• Class Category 50%
• Decision Trees 100%
• Ensemble Method 100%
• Experimental Result 50%
• Extreme Gradient Boosting 100%
• Feature Selection 50%
• Gradient Boosting 50%
• Intrusion Detection 100%
• Intrusion Detection System 50%
• Miss Rate 50%
• Model Elimination 100%
• Random Decision Forest 50%
• Training Process 50%