Hierarchical anomaly detection model for in-vehicle networks using machine learning algorithms

Keyphrases

• Machine Learning Algorithms 100%
• In-vehicle Network 100%
• External Attacks 100%
• Anomaly Detection Model 100%
• Multi-class Classification 66%
• Two-layer 66%
• Hierarchical Classification 66%
• F1 Score 66%
• Multi-labeled 66%
• Multi-class Detection 66%
• Vulnerability 33%
• High-throughput 33%
• Communication Environment 33%
• Network Routing 33%
• Attack Strategy 33%
• Intrusion Detection System 33%
• Detection Model 33%
• Detection Time 33%
• Attack Data 33%
• Evaluation Time 33%
• Hackers 33%
• Learning Spaces 33%
• Data Evaluation 33%
• Message Injection 33%
• Controller Area Network 33%
• Data Learning 33%
• In-vehicle System 33%
• Connectivity Function 33%
• Learning Time 33%
• High-speed Vehicle 33%

Computer Science

• Multiclass Classification 100%
• Multi Class Classification 100%
• Anomaly Detection 100%
• Classification Models 100%
• Machine Learning Algorithm 100%
• High Throughput 50%
• Attack Technique 50%
• Intrusion Detection 50%
• Communication Environment 50%
• Learning Space 50%
• Controller Area Network 50%