卫星导航系统监测站加密流量检测方案

Scheme of Detecting Encrypted Traffic Satellite Navigation

卫星导航系统监测站主要负责卫星定位跟踪、采集、记录和将数据传输到数据中心。为了保障数据的有效性和安全性,必须对数据进行加密后才能传输。面对越来越复杂的网络环境,如何精确,高效,实时地识别出网络加密流量,从而进一步检测出卫星导航加密数据,成为了一个具有挑战性的问题。本文针对加密协议未知,以及网络负载未知的网络加密流量,首先通过分析数据包首部信息,提取出了一组特征属性集——PBF特征集,用于机器学习模型的构建,然后提出了一种以AdaBoost-C4.5算法作为分类器的网络加密流量检测模型,最后通过机器学习方法自动检测加密流量。通过实验验证,该模型在准确率和稳定性上有较好的表现。

The monitoring station of satellite navigation is mainly responsible for satellite tracking,acquisition,recording and transmission of the data to the data center.In order to ensure the effectiveness and security of the data,it is necessary to encrypt the data before it can be transmitted.A variety of encryption technology makes network bandwidth overload.Facing an increasingly complex network environment,how to distinguish the encrypted satellite data from the network traffic accurately,efficiently,timely have become an challenging problem.Aiming at the unknown encryption protocol and unknown network payload,we extracted a set of feature attribute which is called PBF features set by analyzing the header information of the data packet,then we proposed the network encrypted traffic detection model based on AdaBoost_C4.5 algorithm,finally encrypted traffic can be detected automatically by machine learning.The experiment shows that the model has good performance on both accuracy and stability.