基于半监督主动学习的日志异常检测方法OA

In order to ensure the reliability of complex systems,log-based anomaly detection methods have become the focus of research.Existing supervised methods for log anomaly detection require a large amount of labeled data for training,and semi-supervised methods are easily negatively affected by noisy data,and cannot effectively deal with the performance degradation caused by log concept shift.In response to this situation,a log anomaly detection method based on semi-supervised active learning(SSLALog)is proposed,using a supervised Transformer anomaly classification model,and training the model through a combination of semi-supervised self-training learning and active learning.Experimental results show that it outperforms other semi-supervised methods on F1 score,and the practicality is further confirmed by discussing the efficiency.