An efficient method for network security situation assessment
| Autor: | Kaichuan Kong, Sufang Wang, Feng Zhao, Siyan Cheng, Xiaoling Tao |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer Networks and Communications Network security business.industry Computer science General Engineering 020302 automobile design & engineering 02 engineering and technology Computer security computer.software_genre lcsh:QA75.5-76.95 Task (project management) 020901 industrial engineering & automation 0203 mechanical engineering lcsh:Electronic computers. Computer science business computer Situation analysis |
| Zdroj: | International Journal of Distributed Sensor Networks, Vol 16 (2020) |
| ISSN: | 1550-1477 |
| DOI: | 10.1177/1550147720971517 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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