WBHT, a WGAN-LSTM-attention hybrid, achieves F1 0.9250 for black hole anomaly detection on real backbone network data, outperforming twelve baselines.
Toward developing efficient conv-ae-based intrusion detection system using heterogeneous dataset,
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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.