A Undersampling-DoppelGANger Based Data Generation Method for Unbalanced BGP Data

As one of the main border gateway protocol (BGP) security schemes at present, the research on machine learning-based BGP anomaly detection technology faces severe data imbalance due to the lack of BGP anomaly data. To solve this problem, this paper proposes an unbalanced BGP data generation method based on undersampling technology and DoppelGANger, called US-DPGAN. Firstly, random undersampling is performed on majority-class samples to reduce the imbalanced ratio of the dataset and reduce the bias impact of majority-class samples on the data generated by DoppelGANger. Then, we use the trained DoppelGANger model to generate anomaly samples that match the original data distribution and add these samples to the original dataset to create a balanced BGP dataset. Through qualitative and quantitative experiments, we demonstrate that the data generated by the proposed US-DPGAN method better fits the distribution of the original data compared with the original DoppelGANger method. In addition, the anomaly detection model constructed using the US-DPGAN generated data outperforms some other methods in terms of detection accuracy and F1-Score.