LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection

• URL: http://arxiv.org/abs/2309.01189v1
• Date: Sun, 3 Sep 2023 14:22:57 GMT
• Title: LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection
• Authors: Jiaxing Qi, Shaohan Huang, Zhongzhi Luan, Carol Fung, Hailong Yang, Depei Qian
• Abstract summary: We propose 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.
• Score: 35.48151798946824
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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