Semi-supervised anomaly detection in dynamic communication networks

Mathematics

• Anomaly Detection 94%
• Dynamic Networks 87%
• Communication Networks 72%
• Vertex of a graph 34%
• Learning Process 33%
• Inaccurate 29%
• Classifier 27%
• Attack 24%
• Self-learning 21%
• Temporal Correlation 19%
• Experimental Evaluation 17%
• Promotion 16%
• Network Flow 16%
• Demand 12%
• Biased 11%
• Stabilization 10%
• Line 8%
• Demonstrate 7%
• Performance 7%
• Framework 6%

Business & Economics

• Anomaly Detection 100%
• Communication Networks 69%
• Node 58%
• Learning Process 30%
• Classifier 28%
• Attack 18%
• Network Flow 16%
• Regularization 15%
• Stabilization 13%
• Supervision 12%
• Evaluation 6%
• Performance 4%

Engineering & Materials Science

• Anomaly detection 63%
• Telecommunication networks 52%
• Labels 51%
• Classifiers 14%
• Stabilization 8%