Enabling intrusion detection systems with dueling double deep Q-learning

Keyphrases

• Intrusion Detection System 100%
• Reinforcement Learning 100%
• Duel 100%
• Supervised Learning 66%
• Network Architecture 66%
• Network Traffic 66%
• Design Methodology 33%
• Machine Learning 33%
• Historical Data 33%
• K-nearest 33%
• Scarce Resources 33%
• Intrusion Detection 33%
• Data Analytics 33%
• Support Vector Machine 33%
• Computational Resources 33%
• Learning-based 33%
• Representation Learning 33%
• Using Knowledge 33%
• Machine Learning Models 33%
• Accuracy Value 33%
• Knowledge Discovery in Databases 33%
• Exploratory Learning 33%
• Accuracy Performance 33%
• Stochastic Environment 33%
• Statistical Machine Learning 33%
• Deep Reinforcement Learning (deep RL) 33%
• Network Intrusion Detection System 33%
• Model Drift 33%
• Neighborhood Support 33%
• Centres of Expertise 33%
• Machine Learning Learning 33%
• Machine Learning-based Detection 33%
• Classical Machine Learning 33%
• Training Dataset 33%
• Support Center 33%
• Detection-based 33%
• Real Network 33%
• Deep Learning Learning 33%
• Machine Learning Model Training 33%

Computer Science

• Intrusion Detection System 100%
• Reinforcement Learning 100%
• Learning System 100%
• Machine Learning 100%
• Intrusion Detection 75%
• Collected Data 50%
• Supervised Learning 50%
• Network Architecture 50%
• Network Traffic 50%
• Machine Learning Model 50%
• Deep Q-Network 50%
• Support Vector Machine 25%
• Analytic Approach 25%
• Training Dataset 25%
• Data Exploration 25%
• Data Analytics 25%
• Knowledge Discovery in Database 25%
• Computational Resource 25%
• Deep Learning Method 25%
• Convolutional Neural Network 25%