SemiSMAC: A semi-supervised framework for log anomaly detection with automated hyperparameter tuning

Logs generated during software operations are critical for system reliability and anomaly detection. However, their diversity, the scarcity of labeled data, and hyperparameter tuning challenges hinder traditional detection methods. This paper presents SemiSMAC, a novel semi-supervised framework that...

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Vydané v:	Information and software technology Ročník 187; s. 107869
Hlavní autori:	Sun, Yicheng, Keung, Jacky Wai, Yang, Zhen, Liu, Shuo, Liao, Yihan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.11.2025
Predmet:	Anomaly detection Hyperparameter optimization Semi-supervised learning Sequential algorithm configuration Software log analysis Software reliability Sequential algorithm configuration Software log analysis Hyperparameter optimization Software reliability Semi-supervised learning Anomaly detection
ISSN:	0950-5849
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