WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911079397851136 |
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| author | Kaya, Kiymet Ak, Elif Oguducu, Sule Gunduz |
| author_facet | Kaya, Kiymet Ak, Elif Oguducu, Sule Gunduz |
| contents | We propose the Wasserstein Black Hole Transformer (WBHT) framework for detecting black hole (BH) anomalies in communication networks. These anomalies cause packet loss without failure notifications, disrupting connectivity and leading to financial losses. WBHT combines generative modeling, sequential learning, and attention mechanisms to improve BH anomaly detection. It integrates a Wasserstein generative adversarial network with attention mechanisms for stable training and accurate anomaly identification. The model uses long-short-term memory layers to capture long-term dependencies and convolutional layers for local temporal patterns. A latent space encoding mechanism helps distinguish abnormal network behavior. Tested on real-world network data, WBHT outperforms existing models, achieving significant improvements in F1 score (ranging from 1.65% to 58.76%). Its efficiency and ability to detect previously undetected anomalies make it a valuable tool for proactive network monitoring and security, especially in mission-critical networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20373 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks Kaya, Kiymet Ak, Elif Oguducu, Sule Gunduz Machine Learning Artificial Intelligence We propose the Wasserstein Black Hole Transformer (WBHT) framework for detecting black hole (BH) anomalies in communication networks. These anomalies cause packet loss without failure notifications, disrupting connectivity and leading to financial losses. WBHT combines generative modeling, sequential learning, and attention mechanisms to improve BH anomaly detection. It integrates a Wasserstein generative adversarial network with attention mechanisms for stable training and accurate anomaly identification. The model uses long-short-term memory layers to capture long-term dependencies and convolutional layers for local temporal patterns. A latent space encoding mechanism helps distinguish abnormal network behavior. Tested on real-world network data, WBHT outperforms existing models, achieving significant improvements in F1 score (ranging from 1.65% to 58.76%). Its efficiency and ability to detect previously undetected anomalies make it a valuable tool for proactive network monitoring and security, especially in mission-critical networks. |
| title | WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.20373 |