An efficient method for network security situation assessment
Network security situational assessment, the core task of network security situational awareness, can obtain security situation by comprehensively analyzing various factors that affect network status. Thus, network security situational assessment can provide accurate security state evaluation and se...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-11-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720971517 |
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| _version_ | 1849685000259108864 |
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| author | Xiaoling Tao Kaichuan Kong Feng Zhao Siyan Cheng Sufang Wang |
| author_facet | Xiaoling Tao Kaichuan Kong Feng Zhao Siyan Cheng Sufang Wang |
| author_sort | Xiaoling Tao |
| collection | DOAJ |
| description | Network security situational assessment, the core task of network security situational awareness, can obtain security situation by comprehensively analyzing various factors that affect network status. Thus, network security situational assessment can provide accurate security state evaluation and security trend prediction for users. Although plenty of network security situational assessment methods have been proposed, there are still many problems to solve. First, because of high dimensionality of input data, computational complexity in model construction could be very high. Moreover, most of the existing schemes trade computational overhead for accuracy. Second, due to the lack of centralized standard, the weights of indicators are usually determined empirically or by subjective opinions of domain expert. To solve the above problems, we propose a novel network security situation assessment method based on stack autoencoding network and back propagation neural network. In stack autoencoding network and back propagation neural network, to reduce the data storage overhead and improve computational efficiency, we use stack autoencoding network to reduce the dimensions of the indicator data. And the low-dimensional data output by hidden layer of stack autoencoding network will be the input data of the error back propagation neural network. Then, the back propagation neural network algorithm is adopted to perform network security situation assessment. Finally, extensive experiments are conducted to verify the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-bceb7221cb8f46a482939fde11456adf |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2020-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-bceb7221cb8f46a482939fde11456adf2025-08-20T03:23:18ZengWileyInternational Journal of Distributed Sensor Networks1550-14772020-11-011610.1177/1550147720971517An efficient method for network security situation assessmentXiaoling Tao0Kaichuan Kong1Feng Zhao2Siyan Cheng3Sufang Wang4Guangxi Key Laboratory of Cryptography and Information Security, Guilin, ChinaGuangxi Cooperative Innovation Center of Cloud Computing and Big Data, Guilin University of Electronic Technology, Guilin, ChinaGuangxi Cooperative Innovation Center of Cloud Computing and Big Data, Guilin University of Electronic Technology, Guilin, ChinaViterbi School of Engineering, University of Southern California, Los Angeles, CA, USAGuangxi Cooperative Innovation Center of Cloud Computing and Big Data, Guilin University of Electronic Technology, Guilin, ChinaNetwork security situational assessment, the core task of network security situational awareness, can obtain security situation by comprehensively analyzing various factors that affect network status. Thus, network security situational assessment can provide accurate security state evaluation and security trend prediction for users. Although plenty of network security situational assessment methods have been proposed, there are still many problems to solve. First, because of high dimensionality of input data, computational complexity in model construction could be very high. Moreover, most of the existing schemes trade computational overhead for accuracy. Second, due to the lack of centralized standard, the weights of indicators are usually determined empirically or by subjective opinions of domain expert. To solve the above problems, we propose a novel network security situation assessment method based on stack autoencoding network and back propagation neural network. In stack autoencoding network and back propagation neural network, to reduce the data storage overhead and improve computational efficiency, we use stack autoencoding network to reduce the dimensions of the indicator data. And the low-dimensional data output by hidden layer of stack autoencoding network will be the input data of the error back propagation neural network. Then, the back propagation neural network algorithm is adopted to perform network security situation assessment. Finally, extensive experiments are conducted to verify the effectiveness of the proposed method.https://doi.org/10.1177/1550147720971517 |
| spellingShingle | Xiaoling Tao Kaichuan Kong Feng Zhao Siyan Cheng Sufang Wang An efficient method for network security situation assessment International Journal of Distributed Sensor Networks |
| title | An efficient method for network security situation assessment |
| title_full | An efficient method for network security situation assessment |
| title_fullStr | An efficient method for network security situation assessment |
| title_full_unstemmed | An efficient method for network security situation assessment |
| title_short | An efficient method for network security situation assessment |
| title_sort | efficient method for network security situation assessment |
| url | https://doi.org/10.1177/1550147720971517 |
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