Enhancing collaborative intrusion detection via disagreement-based semi-supervised learning in IoT environments

Keyphrases

• Anomaly Detection 25%
• Collaborative Intrusion Detection 100%
• Collaborative Intrusion Detection System 100%
• Copyright 25%
• Data Labeling 25%
• Data Sharing 25%
• Detection Performance 50%
• Disagreement-based Semi-supervised Learning 100%
• False Alarm 25%
• False Alarm Detection 25%
• Ground-truth Labels 25%
• Human Effort 25%
• Human Intervention 25%
• Human Knowledge 25%
• Intercurrent Events 25%
• Internet of Things 25%
• Internet of Things Environment 100%
• Internet of Things Networks 50%
• Knowledge Input 25%
• Labeled Data 25%
• Machine Learning 25%
• Network Environment 25%
• Promising Solutions 25%
• Real World Internet 25%
• Semi-supervised Learning 25%
• Semi-supervised Learning Algorithm 50%
• Single Detector 25%
• Supervised Classifier 25%
• Supervised Learning Classifiers 25%
• Training Examples 25%
• Unlabeled Data 75%

Computer Science

• Anomaly Detection 25%
• Data Instance 25%
• Detecting Intrusion 25%
• Detection Performance 50%
• Exchanging Data 25%
• Experimental Result 25%
• Human Intervention 25%
• Internet of Things 100%
• Intrusion Detection 100%
• Intrusion Detection System 100%
• Labeled Instance 25%
• Learning Algorithm 50%
• Machine Learning 25%
• Network Environment 25%
• Semisupervised Learning 100%
• Supervised Learning 25%
• Training Example 25%
• Unlabeled Data 75%