一种基于XGboost的异常检测算法

An Anomaly Detection Model Based on XGboost

为了提高异常检测的准确性和高效性,提出了基于xgboost的异常检测算法。首先对异常检测当前遇到的挑战进行分析,指出缺少样本和模型泛化是异常检测中的难点。在此基础上设计了异常注入算法,利用3sigma原则对数据集进行扩充;然后设计特征提取器,针对正常数据和异常数据的特点设计相关特征;最后选择xgboost模型对时序数据进行异常检测。此异常检测流程提高了异常检测的准确性和泛化能力。通过在KPI公共数据集上进行实验,验证了该设计的准确性和有效性。

In order to improve the accuracy and efficiency of anomaly detection,an anomaly detection algorithm based on xgboost is proposed.First,analyze the current challenges of anomaly detection,and point out that lack of samples and model generalization are the difficulties in anomaly detection.On this basis,an anomaly injection algorithm is designed,and the data set is expanded us⁃ing the 3sigma principle;then a feature extractor is designed to design related features according to the characteristics of normal da⁃ta and abnormal data;finally,the xgboost model is selected to perform anomaly detection on time series data.This anomaly detec⁃tion process improves the accuracy and generalization ability of anomaly detection.Through experiments on the KPI public data set,the accuracy and effectiveness of the design are verified.