Abstract
As an emerging technology that blocks network security threats, network security situation prediction is the key to defending against network security threats. In view of the single source of information and the lack of time attributes of the existing methods, we propose an optimal network security situation prediction model based on lstm neural network. We employ the stochastic gradient descent method as the minimum training loss to establish a network security situation prediction model, and give the model implementation algorithm pseudo code to further predict the future network security situation. The simulation experiments based on the data collected from Security Data dataset show that compared with other commonly used time series methods, the prediction accuracy of the model is higher and the overall situation of network security situation is more intuitively reflected, which provides a new solution for network security situation.
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Acknowledgment
This work is supported by the NSF of China under grants No. 61702334.
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Chen, L., Fan, G., Guo, K., Zhao, J. (2021). Security Situation Prediction of Network Based on Lstm Neural Network. In: He, X., Shao, E., Tan, G. (eds) Network and Parallel Computing. NPC 2020. Lecture Notes in Computer Science(), vol 12639. Springer, Cham. https://doi.org/10.1007/978-3-030-79478-1_12
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DOI: https://doi.org/10.1007/978-3-030-79478-1_12
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