Summary of Tplogad: Unsupervised Log Anomaly Detection Based on Event Templates and Key Parameters, by Jiawei Lu et al.
TPLogAD: Unsupervised Log Anomaly Detection Based on Event Templates and Key Parameters
by Jiawei Lu, Chengrong Wu
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel unsupervised method for analyzing unstructured logs is proposed in this paper, called TPLogAD. The approach utilizes event templates and key parameters to detect anomalies, which is a more effective way compared to existing methods that rely on indexes of event templates or fixed string parts of templates. The TPLogAD method incorporates two semantic representation techniques: itemplate2vec and para2vec, which enable the detection of anomalies in event templates and parameters respectively. This approach can avoid interference from log diversity and dynamics, leading to improved performance. Experimental results on four public log datasets demonstrate that TPLogAD outperforms existing log anomaly detection methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Log analysis is important for detecting problems with Web service systems. The current method of manually analyzing logs is inefficient and prone to errors. Researchers have developed various methods to analyze logs, including using indexes of event templates or forming vectors based on fixed string parts of templates. However, these methods may not capture all the features and semantic information in log entries, leading to missed or false alarms. This paper proposes a new method called TPLogAD that can analyze unstructured logs and detect anomalies. The approach uses two techniques: itemplate2vec and para2vec, which are efficient and easy to implement. The results show that this method is more effective than existing methods. |
Keywords
» Artificial intelligence » Anomaly detection » Unsupervised
