Industrial Control System Anomaly Detection and Classification Based on Network Traffic

Keyphrases

• Network Traffic 100%
• Classification Basis 100%
• System Anomaly Detection 100%
• Industrial Control Systems 100%
• Anomaly Classification 100%
• High Performance 50%
• Synthetic Minority Oversampling Technique (SMOTE) 50%
• Tomek Links 50%
• Denoising Autoencoder 50%
• F1 Score 25%
• Precision-recall 25%
• Classification Methods 25%
• Anomaly Detection 25%
• Evaluation Results 25%
• Link Mechanism 25%
• Overfitting 25%
• Undersampling 25%
• Ensemble Learning 25%
• Learning Concepts 25%
• Industrial Field 25%
• Convergence Analysis 25%
• XGBoost 25%
• Anomaly Detection System 25%
• Data Noise 25%
• Public Access 25%
• Recall Score 25%
• Precision Score 25%
• Data Imbalance Problem 25%
• Network Traffic Data 25%
• Extreme Gradient Boosting(XGBoost) 25%
• ELECTRA 25%
• Computational Convergence 25%
• Computational Complexity Analysis 25%
• Modbus TCP 25%
• Railway Industry 25%
• Field Protocol 25%

Computer Science

• Network Traffic 100%
• Anomaly Detection 100%
• Industrial Control System 100%
• Extreme Gradient Boosting 75%
• Autoencoder 50%
• de-noising 50%
• Computational Complexity 25%
• Classification Method 25%
• Detection Method 25%
• Good Performance 25%
• Evaluation Result 25%
• Ensemble Learning 25%
• Mathematical Convergence 25%
• Modbus 25%
• Protocol Field 25%
• Railway Industry 25%
• Computational Convergence 25%

Engineering

• Control System 100%
• Anomaly Detection 100%
• Industrial Control 100%
• Denoising Autoencoders 50%
• Applicability 25%
• Computational Complexity 25%
• Link Mechanism 25%
• Real Life 25%
• Classification Method 25%
• Good Performance 25%
• Railway 25%
• Traffic Data 25%