Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems
Author Information
Author(s): Liu Yang, Ren Shaochen, Wang Xuran, Zhou Mengjie
Primary Institution: Worcester Polytechnic Institute
Hypothesis
Can a novel deep learning framework effectively detect anomalies in distributed system logs by integrating temporal sequence modeling with logical dependency analysis?
Conclusion
The TLAN framework significantly improves anomaly detection in distributed systems, achieving a 9.4% increase in F1-score and a 15.3% reduction in false alarms.
Supporting Evidence
- TLAN outperforms existing methods by achieving a 9.4% improvement in F1-score.
- Extensive experiments on a large-scale synthetic dataset validate the effectiveness of TLAN.
- TLAN reduces false alarms by 15.3% while maintaining low latency in real-time detection.
Takeaway
This study created a smart system that can find problems in computer networks by looking at logs, which are like diaries of what the computers do. It works better than older methods.
Methodology
The study used a novel deep learning framework called TLAN that combines temporal sequence modeling and logical dependency analysis for anomaly detection in distributed system logs.
Limitations
The model's performance in detecting network-related anomalies suggests room for improvement in modeling distributed communication patterns.
Statistical Information
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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