Detecting false data injection attacks in peer to peer energy trading using machine learning

Keyphrases

• Machine Learning 100%
• Peer-to-peer Energy Trading 100%
• False Data Injection Attack 100%
• Superior Performance 50%
• Accuracy Rate 50%
• Free Energy 50%
• Instance-based 50%
• Decision-making Method 50%
• World Wide Web 50%
• Potential Risk 50%
• Detection Rate 50%
• Scenario Approach 50%
• Machine Learning Models 50%
• Vulnerability 50%
• Detection Model 50%
• Performance Prediction 50%
• Austin 50%
• Attacker 50%
• Prosumer 50%
• False Negative Rate 50%
• Machine Learning Classifiers 50%
• Attack Detection 50%
• Texas 50%
• Distributed Energy Resources 50%
• Security Breaches 50%
• Interpretable Machine Learning 50%
• Attack Model 50%
• P2P Energy Trading 50%
• Home Energy Management System 50%
• Energy Trading System 50%
• Popular 50%
• Transparent Decision-making 50%
• Threat Scenarios 50%
• Peer-to-peer (P2P) Energy Trading 50%
• Black Box Machine Learning 50%

Computer Science

• False Data Injection Attacks 100%
• Machine Learning 100%
• Learning System 100%
• Superior Performance 33%
• Detection Rate 33%
• Attackers 33%
• False Negative 33%
• Security Breach 33%
• Trading System 33%
• Attack Model 33%
• Threat Scenario 33%
• Decision-Making 33%
• Experimental Result 33%
• Predictive Performance 33%
• Interpretable Machine Learning 33%