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Detecting system anomalies based on log data is important for ensuring thesecurity and reliability of computer systems. Recently, deep learning modelshave been widely used for log anomaly detection. The core idea is to model thelog sequences as natural language and adopt deep sequential models, such asLSTM or Transformer, to encode the normal patterns in log sequences vialanguage modeling. However, there is a gap between language modeling andanomaly detection as the objective of training a sequential model via alanguage modeling loss is not directly related to anomaly detection. To fill upthe gap, we propose LogGPT, a novel framework that employs GPT for log anomalydetection. LogGPT is first trained to predict the next log entry based on thepreceding sequence. To further enhance the performance of LogGPT, we propose anovel reinforcement learning strategy to finetune the model specifically forthe log anomaly detection task. The experimental results on three datasets showthat LogGPT significantly outperforms existing state-of-the-art approaches.

LogGPT: Log Anomaly Detection via GPT