A new method for the prediction of network security situations based on recurrent neural network with gated recurrent unit
International Journal of Intelligent Computing and Cybernetics
ISSN: 1756-378X
Article publication date: 17 January 2020
Issue publication date: 5 May 2020
Abstract
Purpose
The purpose of this paper is to solve the shortage of the existing methods for the prediction of network security situations (NSS). Because the conventional methods for the prediction of NSS, such as support vector machine, particle swarm optimization, etc., lack accuracy, robustness and efficiency, in this study, the authors propose a new method for the prediction of NSS based on recurrent neural network (RNN) with gated recurrent unit.
Design/methodology/approach
This method extracts internal and external information features from the original time-series network data for the first time. Then, the extracted features are applied to the deep RNN model for training and validation. After iteration and optimization, the accuracy of predictions of NSS will be obtained by the well-trained model, and the model is robust for the unstable network data.
Findings
Experiments on bench marked data set show that the proposed method obtains more accurate and robust prediction results than conventional models. Although the deep RNN models need more time consumption for training, they guarantee the accuracy and robustness of prediction in return for validation.
Originality/value
In the prediction of NSS time-series data, the proposed internal and external information features are well described the original data, and the employment of deep RNN model will outperform the state-of-the-arts models.
Keywords
Acknowledgements
The paper is supported by the funds of Ningde Normal University Youth Teacher Research Program (2015Q15) and The Education Science Project of the Junior Teacher in the Education Department of Fujian province (JAT160532).
Citation
Feng, W., Wu, Y. and Fan, Y. (2020), "A new method for the prediction of network security situations based on recurrent neural network with gated recurrent unit", International Journal of Intelligent Computing and Cybernetics, Vol. 13 No. 1, pp. 25-39. https://doi.org/10.1108/IJICC-06-2017-0066
Publisher
:Emerald Publishing Limited
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