WBHT, a WGAN-LSTM-attention hybrid, achieves F1 0.9250 for black hole anomaly detection on real backbone network data, outperforming twelve baselines.
Deep learning for network intrusion: A hierarchical approach to reduce false alarms,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks
WBHT, a WGAN-LSTM-attention hybrid, achieves F1 0.9250 for black hole anomaly detection on real backbone network data, outperforming twelve baselines.