A Self-Updating Hybrid Meta-Learning Framework for IoT Traffic Classification

Keyphrases

• Distribution Shift 100%
• Meta-learning Framework 100%
• Self-updating 100%
• IoT Traffic Classification 100%
• Ensemble Learning 50%
• Random Forest 50%
• Proposed Design 50%
• Traffic Dataset 50%
• Uncertainty-aware 50%
• Tunable Parameters 50%
• Accurate Classification 50%
• Encrypted Traffic 50%
• Feature Analysis 50%
• Hellinger Distance 50%
• Robust Decision Making 50%
• Meta-classifier 50%
• Selective Retraining 50%
• Bayesian Neural Network 50%
• IoT Traffic 50%
• Decision Fusion 50%

Computer Science

• Learning Framework 100%
• Internet-Of-Things 100%
• Meta-Learning 100%
• Traffic Classification 100%
• Experimental Result 50%
• Random Decision Forest 50%
• Neural Network 50%
• Interpretability 50%
• Ensemble Learning 50%