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13 October 2022 Network security situation prediction model based on grey wolf optimization algorithm to optimize support vector machine
Chen Chen, Lintao Yang, Guanghua Wang, Xiaoyan Ye
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Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122870G (2022) https://doi.org/10.1117/12.2640829
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China
Abstract
When making network security situation prediction, researchers usually use historical network security situation values to make predictions. Such predictive models often do not perform well on complex networks with multi-source inputs. Aiming at the problem that the prediction accuracy of the existing prediction models is not high in complex networks, a network security situation prediction model based on grey wolf optimization algorithm to optimize support vector machine is proposed. In this paper, the situation elements are first accumulated, and then the support vector machine is used to independently predict different situation elements in the network, and finally the predicted values are fused into the network security situation value. The parameters of the support vector machine are determined using the grey wolf optimization algorithm. The simulation results show that the model has a good prediction effect.
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Chen Chen, Lintao Yang, Guanghua Wang, and Xiaoyan Ye "Network security situation prediction model based on grey wolf optimization algorithm to optimize support vector machine", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122870G (13 October 2022); https://doi.org/10.1117/12.2640829
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KEYWORDS
Network security
Data modeling
Optimization (mathematics)
Performance modeling
Computer security
Data fusion
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