LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection
This work addresses the problem of automating anomaly detection in high-volume, noisy log data for software systems, but it is incremental as it applies an existing model to a new domain.
The authors tackled log-based anomaly detection by proposing LogGPT, a framework using ChatGPT's language interpretation, and found it shows promising results with good interpretability compared to three deep learning methods on BGL and Spirit datasets.
The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several challenges such as high-dimensional and noisy log data, class imbalance, generalization, and model interpretability. Recently, ChatGPT has shown promising results in various domains. However, there is still a lack of study on the application of ChatGPT for log-based anomaly detection. In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By leveraging the ChatGPT's language interpretation capabilities, LogGPT aims to explore the transferability of knowledge from large-scale corpora to log-based anomaly detection. We conduct experiments to evaluate the performance of LogGPT and compare it with three deep learning-based methods on BGL and Spirit datasets. LogGPT shows promising results and has good interpretability. This study provides preliminary insights into prompt-based models, such as ChatGPT, for the log-based anomaly detection task.