Summary of Logformer: a Pre-train and Tuning Pipeline For Log Anomaly Detection, by Hongcheng Guo et al.
LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection
by Hongcheng Guo, Jian Yang, Jiaheng Liu, Jiaqi Bai, Boyang Wang, Zhoujun Li, Tieqiao Zheng, Bo Zhang, Junran peng, Qi Tian
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a Transformer-based framework called LogFormer for log anomaly detection in artificial intelligence for IT operations (AIOps). The model improves generalization across different domains by pre-training on a source domain and then adapting to a target domain. It also incorporates a Log-Attention module to capture information ignored by traditional log-parsing methods. Experimental results demonstrate the effectiveness of LogFormer, requiring fewer trainable parameters and lower training costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Log anomaly detection is important in AI for IT operations (AIOps). A new model called LogFormer helps identify unusual patterns in log data from different areas. The model learns from one area and then adjusts to another area with less training needed. It also looks at information that’s often missed by usual ways of analyzing logs. This makes it better than previous methods. |
Keywords
* Artificial intelligence * Anomaly detection * Attention * Generalization * Parsing * Transformer
