Yang Yang1,*, Qian Zhao1, Linna Ruan2, Zhipeng Gao1, Yonghua Huo3, Xuesong Qiu1
Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1139-1155, 2020, DOI:10.32604/iasc.2020.011705
Abstract In network anomaly detection, network traffic data are often imbalanced, that is, certain classes of network traffic data have a large sample data
volume while other classes have few, resulting in reduced overall network traffic
anomaly detection on a minority class of samples. For imbalanced data, researchers have proposed the use of oversampling techniques to balance data sets; in particular, an oversampling method called the SMOTE provides a simple and
effective solution for balancing data sets. However, current oversampling methods
suffer from the generation of noisy samples and poor information quality. Hence,
this study proposes an oversampling method for imbalanced… More >