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A Lightweight and Robust Low-Rank Tensor Embedding-Integrated VAE for Network Anomaly Detection
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DOI：10.1109/tnsm.2026.3716561.png)
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Abstract
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As networks scale exponentially, robust anomaly detection has become critical. Although numerous detection algorithms have been proposed, most rely on linear matrix-based models that fail to effectively capture multidimensional structural characteristics (including temporal patterns, node locations, and more) or the underlying nonlinear relationships. Moreover, periodic temporal patterns are often overlooked, degrading detection performance. To address these limitations, we propose a novel Tensor-based Embedding Variational Autoencoder (TEVAE) model. TEVAE introduces a tensor representation framework that integrates deep temporal feature extraction with probabilistic reconstruction. It employs bidirectional long short-term memory networks to distill temporally enriched latent representations from contaminated network tensors. These representations are processed through a probabilistic variational framework, enabling robust learning of latent embeddings and high-fidelity reconstruction of denoised outputs. To further reduce computational demand, we propose LightTEVAE. It decomposes the network tensor via dimension preserved tensor decomposition into low-rank factors, which are embedded as compact representations for encoding, thereby reducing complexity while preserving structural integrity. Experimental results on public network datasets demonstrate that: 1) TEVAE achieves superior anomaly detection performance, consistently outperforming nine mainstream methods by a clear margin; 2) LightTEVAE effectively maintains this high detection performance while substantially reducing computational overhead. These results demonstrate the superiority of both proposed models in temporal feature extraction, complex pattern learning, and large-scale processing efficiency for network anomaly detection.
Keywords:
Low-rank tensor decomposition
network traffic data
anomaly detection
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