System log anomaly detection based on contrastive learning and retrieval augmented

System logs are run-time significant events of computer systems recorded by software. By analyzing the system logs, a lot of important information and issues can be detected promptly. Log anomaly detection is a popular research topic in recent years. However, log anomaly detection faces lots of challenges such as the variability of logs, imbalance of normal and abnormal records in log, the continuous emergence of new log formats. To address these challenges, we propose a log anomaly detection framework named LogSentry based on contrastive learning and retrieval-augmented. Our framework consists of a training phase and an inference phase. In the training phase, a BERT based log anomaly detection model using contrastive learning is pre-trained and fine-tuned. In the inference phase, a retrieval-augmented method based on KNN is introduced. During inference, the prediction result of the log anomaly detection model and the average output of the retrieval-augmented method based on KNN will do a weighted summation to obtain the final result, which is whether the log data is abnormal or normal. Our experiments on widely used log datasets indicate the solution proposed in this paper achieves high performance over baseline methods.