用生成注意力模型检测网络黑洞，提升异常发现能力。
WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks
- 融合生成模型与注意力机制，稳定训练并精准识别异常
- 在真实数据上F1得分提升1.65%至58.76%
- 适合需要主动监控的高可靠网络系统
我们提出Wasserstein黑洞变换器（WBHT）框架，用于检测通信网络中的黑洞（BH）异常。这类异常导致数据包丢失但无故障通知，破坏连接性并引发经济损失。WBHT结合生成建模、序列学习与注意力机制，提升黑洞异常检测能力。其将Wasserstein生成对抗网络与注意力机制融合，实现稳定训练与准确识别；采用长短期记忆层捕捉长期依赖，卷积层提取局部时序模式；通过潜在空间编码机制区分异常网络行为。在真实网络数据上测试，性能优于现有模型，F1得分提升1.65%至58.76%。该模型具备高效性与发现未知异常的能力，适用于关键任务网络的主动监控与安全防护。
原文摘要 · Abstract (English)
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.
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