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论文:2020,Vol:38,Issue(2):341-350 |
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引用本文： |
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吕娜, 周家欣, 冯煊, 陈柯帆, 陈旿. 一种时效增强的机载网络流量识别方法[J]. 西北工业大学学报 |
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LYU Na, ZHOU Jiaxin, FENG Xuan, CHEN Kefan, CHEN Wu. A Timeliness-Enhanced Traffic Identification Method in Airborne Network[J]. Northwestern polytechnical university |
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| 一种时效增强的机载网络流量识别方法 |
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| 吕娜1, 周家欣1, 冯煊2, 陈柯帆1, 陈旿3 |
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1. 空军工程大学 信息与导航学院, 陕西 西安 710077;
2. 中国人民解放军31006部队, 北京 100000;
3. 西北工业大学 网络空间安全学院, 陕西 西安 710072 |
| 摘要: |
| 机载网络拓扑动态性强，带宽受限等特点导致其难以为多样化的航空集群作战任务提供可靠的信息交互服务，因此需要对网络中的"大流量对象"进行实时识别，从而优化流量控制，提升网络性能。针对该问题，基于机器学习贝叶斯模型，提出一种时效增强的流量识别方法，首先通过对原始流量数据集进行预处理得到数据流训练子集，并基于贝叶斯网络模型构造子分类器，然后基于多窗口动态贝叶斯网络分类器模型实现大流量对象的早期识别。仿真结果表明，相较于现有的大流识别方法，所提方法可以在保证识别准确性的条件下有效提升识别时效性。 |
| 关键词:
流量识别
机器学习
贝叶斯网络
航空集群
机载网络
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| A Timeliness-Enhanced Traffic Identification Method in Airborne Network |
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| LYU Na1, ZHOU Jiaxin1, FENG Xuan2, CHEN Kefan1, CHEN Wu3 |
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1. 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 |
| Abstract: |
| 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
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| 收稿日期: 2019-06-21
修回日期:
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| DOI: 10.1051/jnwpu/20203820341 |
| 基金项目: 陕西省重点研发计划（2017GY-069）资助 |
| 通讯作者:
Email： |
| 作者简介: 吕娜(1970-),女,空军工程大学教授,主要从事航空数据链、通信网络研究。
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| 作者相关文章 |
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| 吕娜 在本刊中的所有文章 |
| 周家欣 在本刊中的所有文章 |
| 冯煊 在本刊中的所有文章 |
| 陈柯帆 在本刊中的所有文章 |
| 陈旿 在本刊中的所有文章 |
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