Using Ensemble Learning for Anomaly Detection in Cyber–Physical Systems

Keyphrases

• Anomaly Detection 100%
• Ensemble Learning 100%
• Cyberphysical Systems 100%
• Technological Networks 42%
• Information Technology Networks 42%
• Operational Technology 42%
• Threshold Method 42%
• Machine Learning Algorithms 28%
• Life Safety 28%
• System Environment 28%
• Signature-based 28%
• Vulnerability 14%
• Divide-and-conquer 14%
• Performance Prediction 14%
• Detection Method 14%
• Traditional Machine Learning 14%
• Attack Surface 14%
• Security Threats 14%
• Machine Learning Based 14%
• Historical Data 14%
• Machine Learning Classifiers 14%
• Transfer Learning 14%
• Network Technology 14%
• Swift 14%
• Training Model 14%
• Accuracy Improvement 14%
• Industry 4.0 (I4.0) 14%
• Machine Learning Models 14%
• Research Datasets 14%
• Computationally Expensive 14%
• Economic Safety 14%
• Anomaly Detection System 14%
• Hybrid Methodology 14%
• Air Gap 14%
• Trust Network 14%
• System Availability 14%
• Safety Cost 14%
• Public Research 14%
• Hybrid Anomaly 14%
• Multiple Datasets 14%
• IoT Applications 14%
• Cyber Threats 14%
• Financial Safety 14%
• Multiple Machines 14%
• Simple Threshold 14%

Computer Science

• Anomaly Detection 100%
• Ensemble Learning 100%
• Anomaly-Based Detection 100%
• Network Technology 75%
• Machine Learning 75%
• Learning System 75%
• Information Technology 75%
• Machine Learning Algorithm 50%
• Historical Data 25%
• Training Model 25%
• Industry 4.0 25%
• Attack Surface 25%
• Transfer Learning 25%
• Experimental Result 25%
• Trusted Network 25%
• Domain Knowledge 25%
• System Availability 25%
• Multiple Machine 25%
• Predictive Performance 25%