<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Aiming at the lack of subjectivity of the network security situation assessment method and the complexity and non-linearity of data obtained through situational factors, a fuzzy neural network security situation which is optimised based on an improved gravitational search algorithm combined with fractional differential equation analysis, as an Evaluation model, is proposed. In order to quickly and accurately predict the situation value of the network security situation at that moment, a method for situation prediction of long-term and short-term memory networks based on an improved Nadam algorithm to optimise the online update mechanism is proposed. Note that the situation time series obtained from online assessment cannot be used in a better and efficient manner. The model can minimise the cost function and update the model more effectively by updating the model parameters online Prediction accuracy. In order to improve the problem of slow convergence speed during model network training, the Look-ahead method is used to improve Nesterov's adaptive gradient momentum estimation algorithm to accelerate the model's convergence. Finally, the simulation results analyse and compare the prediction model, which not only improves the convergence speed of the prediction model, but also greatly reduces the prediction error of the model.</p>
</abstract>

Aiming at the lack of subjectivity of the network security situation assessment method and the complexity and non-linearity of data obtained through situational factors, a fuzzy neural network security situation which is optimised based on an improved gravitational search algorithm combined with fractional differential equation analysis, as an Evaluation model, is proposed. In order to quickly and accurately predict the situation value of the network security situation at that moment, a method for situation prediction of long-term and short-term memory networks based on an improved Nadam algorithm to optimise the online update mechanism is proposed. Note that the situation time series obtained from online assessment cannot be used in a better and efficient manner. The model can minimise the cost function and update the model more effectively by updating the model parameters online Prediction accuracy. In order to improve the problem of slow convergence speed during model network training, the Look-ahead method is used to improve Nesterov's adaptive gradient momentum estimation algorithm to accelerate the model's convergence. Finally, the simulation results analyse and compare the prediction model, which not only improves the convergence speed of the prediction model, but also greatly reduces the prediction error of the model.

Network monitoring and processing accuracy of big data acquisition based on mathematical model of fractional differential equation

Department of Information System, Faculty of Computing and Information Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Aiming at the lack of subjectivity of the network security situation assessment method and the complexity and non-linearity of data obtained through...

Network monitoring and processing accuracy of big data acquisition based on mathematical model of fractional differential equation

Pubblicato online: 27 dic 2021

Pagine: 327 - 340

Ricevuto: 17 giu 2021

Accettato: 24 set 2021

DOI: https://doi.org/10.2478/amns.2021.2.00118

Parole chiave
fractional differential equation, big data acquisition, network monitoring

© 2021 Ming Si et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Network monitoring and processing accuracy of big data acquisition based on mathematical model of fractional differential equation

Pubblicato online: 27 dic 2021

Pagine: 327 - 340

Ricevuto: 17 giu 2021

Accettato: 24 set 2021

DOI: https://doi.org/10.2478/amns.2021.2.00118

Parole chiavefractional differential equation, big data acquisition, network monitoring

© 2021 Ming Si et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
fractional differential equation, big data acquisition, network monitoring