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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Bibliographic Details

Published in:	Information and software technology Vol. 187; p. 107869
Main Authors:	Sun, Yicheng, Keung, Jacky Wai, Yang, Zhen, Liu, Shuo, Liao, Yihan
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.11.2025
Subjects:	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
Online Access:	Get full text
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