Lightweight Multi-Task Learning Method for System Log Anomaly Detection
Log anomaly detection is a crucial task in monitoring IT systems along with metrics and traces. An anomaly could be detected by either one of two types of logs: individual logs or log sequences. While an individual log indicates an independent system status, combining multiple logs describes the exe...
Saved in:
| Main Authors: | Tuan-Anh Pham, Jong-Hoon Lee |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10589616/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Improvement of multi-parameter anomaly detection method: Addition of a relational token between parameters
by: Hironori Uchida, et al.
Published: (2025-01-01) -
LogRESP-Agent: A Recursive AI Framework for Context-Aware Log Anomaly Detection and TTP Analysis
by: Juyoung Lee, et al.
Published: (2025-06-01) -
Anomaly Detection Algorithms for Real-Time Log Data Analysis at Scale
by: Andras Horvath, et al.
Published: (2025-01-01) -
Temporal Decay Loss for Adaptive Log Anomaly Detection in Cloud Environments
by: Lelisa Adeba Jilcha, et al.
Published: (2025-04-01) -
Research on system log anomaly detection based on deep learning
by: Yidong WANG, et al.
Published: (2019-10-01)