图嵌入和LSTM自动编码器结合的BGP异常检测  

Anomaly Detection of BGP Using Graph Embedding and LSTM AutoEncoder

作　　者：张树晓 唐勇[1] 刘宇靖[1] ZHANG Shu-Xiao;TANG Yong;LIU Yu-Jing(College of Computer Science and Technology,National University of Defence Technology,Changsha 410073,China)

机构地区：[1]国防科技大学计算机学院,长沙410073

出　　处：《计算机系统应用》2022年第2期246-252,共7页Computer Systems & Applications

基　　金：国家自然科学基金(61602503)。

摘　　要：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,发现异常数据.在实际的异常事件数据中,该方法成功检测到了异常数据,并且相比传统的检测方法有较高的准确率.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.

关 键 词：图嵌入特征 BGP 异常检测 LSTM自动编码器 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP393.08[自动化与计算机技术—控制科学与工程]