图嵌入和LSTM自动编码器结合的BGP异常检测

针对BGP异常数据的检测问题,依托互联网公开的真实BGP更新报文数据,重点结合网络的拓扑特征及时序变化特点,提出一种新的基于图嵌入特征和LSTM自动编码器的BGP异常检测方法.首先利用BGP数据中AS_PATH属性信息,构建基于时间序列的网络拓扑图的动态嵌入特征数据集,然后使用LSTM自动编码器模型对数据进行检测,发现...

Anomaly Detection of BGP Using Graph Embedding and LSTM AutoEncoder

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.