Host-based intrusion detection with multi-datasource and deep learning

Keyphrases

• Deep Learning 100%
• Host-Based Anomaly Detection 100%
• Attack Stage 100%
• Host-based 50%
• Attack Strategy 50%
• Attack Detection 50%
• Memory Faults 50%
• System Log 50%
• Network Applications 25%
• Detection Method 25%
• Classification Performance 25%
• System Resources 25%
• Log Data 25%
• Machine Learning 25%
• Detection Efficiency 25%
• Convolutional Neural Network 25%
• Intrusion Detection System 25%
• Deep Neural Network 25%
• System Effectiveness 25%
• Highly Effective 25%
• Attention Model 25%
• Control Stage 25%
• Traffic System 25%
• Improved Detection 25%
• Automatic Feature Selection 25%
• Initial Access 25%
• Temporal Relations 25%
• Hackers 25%
• Long Short-term Memory 25%
• Virtual Memory 25%
• Network Traffic Data 25%
• XGBoost 25%
• Combined Detection 25%
• F1 Score 25%
• Command-and-control 25%
• Attack Feature 25%
• Advanced Attacks 25%
• Resident Memory 25%
• Network Host 25%
• Relationships Exploration 25%

Computer Science

• Intrusion Detection 100%
• Data Source 100%
• Deep Learning Method 100%
• Experimental Result 50%
• Convolutional Neural Network 50%
• Classification Performance 50%
• Network Traffic 50%
• Log Data 50%
• Data Type 50%
• Intrusion Detection System 50%
• Deep Neural Network 50%
• Detection Process 50%
• Feature Selection 50%
• System Resource 50%
• Temporal Relationship 50%
• Long Short-Term Memory Network 50%
• Initial Access 50%
• System Effectiveness 50%
• Extreme Gradient Boosting 50%
• Affected System 50%
• Virtual Memory 50%
• Machine Learning 50%
• Learning System 50%
• Feature Extraction 50%