LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection
Jiaxing Qi Shaohan Huang Zhongzhi Luan Carol Fung Hailong Yang Depei Qian
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.