Volume 38, Number 2, April 2020
|Page(s)||341 - 350|
|Published online||17 July 2020|
A Timeliness-Enhanced Traffic Identification Method in Airborne Network
School of Information and Navigation, Air Force Engineering University, Xi'an 710077, China
2 PLA 31006 Troops, Beijing 100000, China
3 School of Cybersecurity, Northwestern Polytechnical University, Xi'an 710072, China
High dynamic topology and limited bandwidth of the airborne network make it difficult to provide reliable information interaction services for diverse combat mission of aviation swarm operations. Therefore, it is necessary to identify the elephant flows in the network in real time to optimize the process of traffic control and improve the performance of airborne network. Aiming at this problem, a timeliness-enhanced traffic identification method based on machine learning Bayesian network model is proposed. Firstly, the data flow training subset is obtained by preprocessing the original traffic dataset, and the sub-classifier is constructed based on Bayesian network model. Then, the multi-window dynamic Bayesian network classifier model is designed to enable the early identification of elephant flow. The simulation results show that compared with the existing elephant flow identification method, the proposed method can effectively improve the timeliness of identification under the condition of ensuring the accuracy of identification.
Key words: traffic classification / machine learning / Bayesian network / aeronautic swarm / airborne network / model / simulation / identification of elephant flow
关键字 : 流量识别 / 机器学习 / 贝叶斯网络 / 航空集群 / 机载网络
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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