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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Information and software technology Jg. 187; S. 107869
Hauptverfasser:	Sun, Yicheng, Keung, Jacky Wai, Yang, Zhen, Liu, Shuo, Liao, Yihan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.11.2025
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!