Research on Intrusion Detection Method Based on Improved Smote and XGBoost
Peihuang Su, Yanhua Liu, Xiang Song · 2018
In view of the unbalanced data set, the original intrusion detection method cannot accurately detect the network security intrusion behavior. An improved smote and XGBoost intrusion detection method is proposed. In terms of the diversity of security data types, the data is processed by the use of one-hot encoding. In terms of data imbalance, using the improved smote technique to oversample for the class with a small amount of data and downsample for a class with a large amount of data. Finally, using the KDD99 security data set to conduct experiments and calculate the missing rate, average precision, average recall rate and F-Score of the model, and compare with the original training method results. The results show that the method has high accuracy and low missing rate in intrusion detection.