| COMPUTER SCIENCE AND TECHNOLOGY |
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Abnormal traffic flow identification for a measurement and control network for launch vehicles |
| XU Hongping1, LIU Yang1, YI Hang1, YAN Xiaotao1, KANG Jian1, ZHANG Wenjin2 |
1. Beijing Institute of Astronautical System Engineering, Beijing 100076, China;
2. PLA 96616 Force, Beijing 100085, China |
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Abstract The measurement and control network of a launch vehicle is an important national defense information infrastructure for remote measurements and launch control. This network provides a key measure to detect abnormal behavior and ensure information security through accurate analysis of the traffic. This paper describes a network strategy using the port mapping method, payload matching, and support vector machine (SVM) learning algorithm. The training samples are produced by the port mapping and payload matching method. Then, the key features are selected based on the information gain. Next, the SVM model is built with these features and trained by the training samples. The traffic data is then analyzed by the voting mechanism. Actual data from the network is used to verify the method with the results showing that this method has an accuracy of 99.1% with far fewer manual analyses.
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| Keywords
measurement and control network of launch vehicle
port mapping
payload matching
dynamic strategy
support vector machine (SVM) learning
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Issue Date: 15 January 2018
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2018,
Vol. 58
