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.

翻译：随着软件密集型系统产生的日志数据量不断增加，手动分析这些数据变得不切实际。基于深度学习的日志异常检测方法已被广泛提出，但面临着高维噪声日志数据、类别不平衡、泛化能力以及模型可解释性等挑战。近期，ChatGPT在多个领域展现出令人瞩目的成果，然而其在日志异常检测中的应用研究仍存在空白。本文提出基于ChatGPT的日志异常检测框架LogGPT，通过利用ChatGPT的语言理解能力，探索大规模语料库知识向日志异常检测任务的迁移性。我们在BGL和Spirit数据集上开展实验，评估LogGPT的性能，并将其与三种深度学习基线方法进行比较。实验结果表明，LogGPT展现出优异的效果并具备良好的可解释性。本研究为基于提示模型（如ChatGPT）的日志异常检测任务提供了初步见解。