Enhancing Log Anomaly Detection through Knowledge Graph Integration
Journal
2024 IEEE 18th International Conference on Semantic Computing (ICSC)
Start Page
204
End Page
207
ISSN
2472-9671
2325-6516
ISBN
9798350385359
Date Issued
2024-02-05
Author(s)
DOI
10.1109/ICSC59802.2024.00038
Abstract
Anomalies of software systems cause inconvenience for users and further lead to significant financial losses for service providers. Detecting such anomalies is therefore crucial. While different approaches have been applied to system logs for anomaly detection, few studies explore graph-based models. In this paper, we introduce a novel log anomaly detection system that combines techniques of knowledge graph learning and recurrent deep learning. We treat log templates extracted from log data as entity nodes in a knowledge graph with these nodes being connected by their connectivity and position relations. By deriving node and relation embeddings, distance scores of log template sequences can be calculated and fed into an LSTM-based classifier to identify system anomalies. The experimental results based on a substantial dataset demonstrate our model’s superior performance in terms of precision, recall, and F1 measures compared to state-of-the-art methods.
Event(s)
18th IEEE International Conference on Semantic Computing, ICSC 2024
Subjects
Deep learning
Semantics
Knowledge graphs
Software systems
Software reliability
Anomaly detection
Publisher
IEEE
Description
18th IEEE International Conference on Semantic Computing, ICSC 2024 - Hybrid, Laguna Hills, United States
Duration: Feb 5 2024 → Feb 7 2024
Duration: Feb 5 2024 → Feb 7 2024
Type
conference proceedings