FML-AD: A Federated Learning Framework With Meta-Model Refinement for Cyberattack Duration Prediction in 5G O-RAN

Keyphrases

• 5G Radio Access Network 25%
• 5G-V2X 25%
• Adaptive Attack 25%
• Attack Duration 25%
• Attack Surface 25%
• CICIoT2023 Dataset 25%
• Cyber Threats 25%
• Cyber-attacks 100%
• Deployment Distributions 25%
• Disruption Mechanism 25%
• Distributed Environment 25%
• Distribution Shift 25%
• Domain Data 25%
• Duration Prediction 100%
• Extensive Evaluation 25%
• Federated Learning Framework 100%
• Fifth Generation 5G 25%
• Flood Scenarios 25%
• Information Privacy 25%
• Jamming 25%
• Metamodel 100%
• Mitigation Strategies 25%
• Model Improvement 100%
• Network Reliability 25%
• Network Testbed 50%
• Network Users 25%
• Open Radio Access Network (O-RAN) 100%
• Operating Point 25%
• Post-detection 25%
• Prediction Accuracy 25%
• Prediction Error 25%
• Prediction pipeline 50%
• Prediction Results 25%
• Proactive Mitigation 25%
• Proof of Concept Study 25%
• Quality of Service 25%
• Radio Access Network Architecture 25%
• Radio Jamming 25%
• Regression Model 25%
• Reliability Data 25%
• Service Network 25%
• SYN Flood 25%
• Target Domain 25%
• Temporal Learning 25%
• Transfer Learning 25%
• Transferability 25%
• Transformer 25%
• UDP Flooding 25%
• Virtual Environment 25%
• XGBoost 25%

Computer Science

• Attack Surface 25%
• Cyber Attack 100%
• Data Domain 25%
• Data Privacy 25%
• Distributed Environment 25%
• Extreme Gradient Boosting 25%
• Federated machine learning 100%
• Learning Framework 100%
• Mitigation Strategy 25%
• Model Refinement 100%
• Network Architecture 25%
• Network Reliability 25%
• Prediction Accuracy 25%
• Prediction Error 25%
• Proof 25%
• Quality of Service 25%
• Radio Access Network 100%
• Testbed 50%
• Transfer Learning 25%
• User Data 25%
• Virtualized Environment 25%