STLAD: Sparse-Attention and Temporal- Decomposition for Scalable Log Anomaly Detection

Log anomaly detection is essential for maintaining the reliability and security of computing systems. Existing methods are hindered by high computational complexity and insufficient modeling of temporal patterns, which limits their scalability and sensitivity. To address these limitations, we presen...

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Bibliografski detalji

Glavni autori:	Yucheng Xie, Ying Wang, Kai Feng, Le Chen, Jibin Wang
Format:	Artigo
Jezik:	Inglês
Izdano:	IEEE 2026-01-01
Serija:	IEEE Access
Teme:	System logs anomaly detection masked language model temporal decomposition self-attention
Online pristup:	https://ieeexplore.ieee.org/document/11373374/
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