WBHT: A Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks

TL;DR

WBHT is a novel generative attention-based framework that effectively detects black hole network anomalies by combining Wasserstein GANs, LSTM, and convolutional layers, outperforming existing methods in real-world data.

Contribution

The paper introduces WBHT, a new generative attention architecture that integrates Wasserstein GANs with sequential and local pattern learning for improved anomaly detection.

Findings

Significant F1 score improvements over existing models.

Effective detection of previously unseen anomalies.

Stable training achieved with Wasserstein adversarial network.

Abstract

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.