Linux log anomaly detection method based on improved gradient lifting decision tree algorithm

To improve the efficiency of log anomaly detection in the Linux operating system, this paper proposes a Linux log anomaly detection method based on an improved gradient lifting decision tree algorithm. Based on the traditional gradient lifting decision tree, this method introduces weight adjustment based on negative gradient, which can give higher weight to abnormal samples when processing log data so that the model pays more attention to a few categories of samples. Experimental results show that the proposed method is more efficient than traditional Linux log anomaly detection methods and can effectively detect potential security threats and abnormal behaviors.