A Novel Fractional Accumulative Grey Model with GA-PSO Optimizer and Its Application
The prediction of cyber security situation plays an important role in early warning against cyber security attacks. The first-order accumulative grey model has achieved remarkable results in many prediction scenarios. Since recent events have a greater impact on future decisions, new information sho...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/2/636 |
| _version_ | 1827622192370679808 |
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| author | Ruixiao Huang Xiaofeng Fu Yifei Pu |
| author_facet | Ruixiao Huang Xiaofeng Fu Yifei Pu |
| author_sort | Ruixiao Huang |
| collection | DOAJ |
| description | The prediction of cyber security situation plays an important role in early warning against cyber security attacks. The first-order accumulative grey model has achieved remarkable results in many prediction scenarios. Since recent events have a greater impact on future decisions, new information should be given more weight. The disadvantage of first-order accumulative grey models is that with the first-order accumulative method, equal weight is given to the original data. In this paper, a fractional-order cumulative grey model (FAGM) is used to establish the prediction model, and an intelligent optimization algorithm known as particle swarm optimization (PSO) combined with a genetic algorithm (GA) is used to determine the optimal order. The model discussed in this paper is used for the prediction of Internet cyber security situations. The results of a comparison with the traditional grey model GM(1,1), the grey model GM(1,n), and the fractional discrete grey seasonal model FDGSM(1,1) show that our model is suitable for cases with insufficient data and irregular sample sizes, and the prediction accuracy and stability of the model are better than those of the other three models. |
| first_indexed | 2024-03-09T11:18:04Z |
| format | Article |
| id | doaj.art-4bb09eb4da8343438155b20755c99992 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:18:04Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4bb09eb4da8343438155b20755c999922023-12-01T00:25:01ZengMDPI AGSensors1424-82202023-01-0123263610.3390/s23020636A Novel Fractional Accumulative Grey Model with GA-PSO Optimizer and Its ApplicationRuixiao Huang0Xiaofeng Fu1Yifei Pu2College of Computer Science, Sichuan University, Chengdu 610065, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing 211103, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, ChinaThe prediction of cyber security situation plays an important role in early warning against cyber security attacks. The first-order accumulative grey model has achieved remarkable results in many prediction scenarios. Since recent events have a greater impact on future decisions, new information should be given more weight. The disadvantage of first-order accumulative grey models is that with the first-order accumulative method, equal weight is given to the original data. In this paper, a fractional-order cumulative grey model (FAGM) is used to establish the prediction model, and an intelligent optimization algorithm known as particle swarm optimization (PSO) combined with a genetic algorithm (GA) is used to determine the optimal order. The model discussed in this paper is used for the prediction of Internet cyber security situations. The results of a comparison with the traditional grey model GM(1,1), the grey model GM(1,n), and the fractional discrete grey seasonal model FDGSM(1,1) show that our model is suitable for cases with insufficient data and irregular sample sizes, and the prediction accuracy and stability of the model are better than those of the other three models.https://www.mdpi.com/1424-8220/23/2/636prediction of cyber security situationfractional ordergrey modelGA-PSO |
| spellingShingle | Ruixiao Huang Xiaofeng Fu Yifei Pu A Novel Fractional Accumulative Grey Model with GA-PSO Optimizer and Its Application Sensors prediction of cyber security situation fractional order grey model GA-PSO |
| title | A Novel Fractional Accumulative Grey Model with GA-PSO Optimizer and Its Application |
| title_full | A Novel Fractional Accumulative Grey Model with GA-PSO Optimizer and Its Application |
| title_fullStr | A Novel Fractional Accumulative Grey Model with GA-PSO Optimizer and Its Application |
| title_full_unstemmed | A Novel Fractional Accumulative Grey Model with GA-PSO Optimizer and Its Application |
| title_short | A Novel Fractional Accumulative Grey Model with GA-PSO Optimizer and Its Application |
| title_sort | novel fractional accumulative grey model with ga pso optimizer and its application |
| topic | prediction of cyber security situation fractional order grey model GA-PSO |
| url | https://www.mdpi.com/1424-8220/23/2/636 |
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