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Anomaly Intrusion Detection in Online Learning Space Based on XGBoost

To ensure the network security of the online learning space, this paper proposes a network anomaly intrusion detection method based on XGBoost that can be applied to the online learning space. Considering about the high latitude and non-linear characteristics of abnormal network traffic data in real scenarios, this method combines multiple weak classifiers into a stronger classifier through an ensemble learning method to solve the dimensional disaster and low operation efficiency of traditional machine learning algorithms. Compared with the traditional ensemble learning methods, this method adds a regularization operator, and uses the second-order approximation of the loss function to select features at the intermediate nodes. These make the method more powerful in detection performance and efficiency. To examine the effect of the proposed method, this paper uses a public data set for validation. In the experiments, the AUC value is 0.9957, and the trend of the ROC curve points out that the method can effectively detect anomaly attacks.