Abstract:With the real border gateway protocol (BGP) update message data disclosed on the Internet, this study proposes a new BGP anomaly detection method based on graph embedding features and long short-term memory (LSTM) AutoEncoder, which focuses on the network topology and variation characteristics in time series. First, the AS_PATH attribute information of BGP data is used to construct a dynamic embedding feature dataset based on the network topology of time series, and then the LSTM AutoEncoder model is employed for data detection to find abnormal ones. For the actual data of abnormal events, the method successfully detects the abnormal data and has higher accuracy than traditional detection methods.