Document Type
Article
Source of Publication
Computer Systems Science and Engineering
Publication Date
1-1-2022
Abstract
System logs record detailed information about system operation and are important for analyzing the system's operational status and performance. Rapid and accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more and more complex, and the number of system logs gradually increases, which brings challenges to analyze system logs. Some recent studies show that logs can be unstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a long time to train models. Therefore, to reduce the computational cost and avoid log instability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takes original log messages as input to avoid the noise. LogUAD uses Word2Vec to generate word vectors and generates weighted log sequence feature vectors with TF-IDF to handle the evolution of log statements. At last, a computationally efficient unsupervised clustering is exploited to detect the anomaly. We conducted extensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25% compared to LogCluster.
DOI Link
ISSN
Publisher
Computers, Materials and Continua (Tech Science Press)
Volume
41
Issue
3
First Page
1207
Last Page
1222
Disciplines
Computer Sciences
Keywords
Feature extraction, Log anomaly detection, Log instability, Word2Vec
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Wang, Jin; Zhao, Changqing; He, Shiming; Gu, Yu; Alfarraj, Osama; and Abugabah, Ahed, "LogUAD: Log unsupervised anomaly detection based on word2Vec" (2022). All Works. 4655.
https://zuscholars.zu.ac.ae/works/4655
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series