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.

通过ChatGPT的语言解释能力，本研究提出了基于ChatGPT的LogGPT框架，旨在探索将大规模语料库的知识转移应用于基于日志的异常检测。在BGL和Spirit数据集上，通过与三种基于深度学习的方法进行比较，实验评估了LogGPT的性能，显示出良好的可解释性和有希望的结果。该研究初步揭示了ChatGPT等基于提示的模型在基于日志的异常检测任务中的潜力。

LogGPT：基于日志的异常检测的ChatGPT探索