Summary of Fastlogad: Log Anomaly Detection with Mask-guided Pseudo Anomaly Generation and Discrimination, by Yifei Lin et al.
FastLogAD: Log Anomaly Detection with Mask-Guided Pseudo Anomaly Generation and Discrimination
by Yifei Lin, Hanqiu Deng, Xingyu Li
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes FastLogAD, a generator-discriminator framework for fast log anomaly detection. The existing unsupervised methods require additional abnormal data or auxiliary datasets, which can be time-consuming and inefficient. FastLogAD addresses this issue by generating pseudo-abnormal logs through the Mask-Guided Anomaly Generation (MGAG) model and efficiently identifying anomalous logs via the Discriminative Abnormality Separation (DAS) model. The MGAG model replaces randomly masked tokens in a normal sequence with unlikely candidates, while the DAS model learns to separate normal and pseudo-abnormal samples based on their embedding norms. This allows for threshold selection without exposure to test data, achieving competitive performance. FastLogAD outperforms existing anomaly detection approaches and achieves at least x10 speed increase compared to prior work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to detect unusual behavior in computer logs. It’s like trying to spot a strange message in a huge pile of normal messages. The current methods for doing this are slow and require a lot of extra data. This new method, called FastLogAD, is faster and can work with less data. It does this by creating fake abnormal messages that look like they could be real, but aren’t. Then it uses these fake messages to train a model that can quickly spot the real abnormal messages. The paper shows that FastLogAD works better than other methods and can do its job much faster. |
Keywords
» Artificial intelligence » Anomaly detection » Embedding » Mask » Unsupervised
