BGP ANOMALY DETECTION METHOD BASED ON DEEP FOREEST

For a long time, BGP abnormal events have seriously affected the stability and security of the Internet, so the research on BGP anomaly detection algorithms is particularly important. In view of the low accuracy of the machine learning algorithm that has been applied to BGP anomaly detection and the single type of anomaly in the experimental data set, in order to improve the accuracy and universality of the algorithm, an anomaly classification algorithm based on deep forest is introduced. The experiment used multiple abnormal event data sets, and eliminated redundant and irrelevant features based on Pearson correlation coefficients, which were used to classify BGP anomalies. The deep forest algorithm and other machine learning algorithms were used to classify the data. Comprehensive experimental results show that the performance of deep forest is better than other algorithms.