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A Trusted 3.0 Network Security Risk Assessment and Decision Support System Based on Discrete Rough Sets

Weijia Su

Weijia Su

Jilin Jineng Electric Power Communication Co., LtdChangchun, China
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Su, Weijia
, 
Shengda Wang

Shengda Wang

Jilin Information & Telecommunication Company, State Grid Jilin Electric Power Corporation LtdChangchun, China
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Wang, Shengda
, 
Danni Liu

Danni Liu

Jilin Information & Telecommunication Company, State Grid Jilin Electric Power Corporation LtdChangchun, China
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Liu, Danni
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Xiaofu Sun

Xiaofu Sun

Jilin Jineng Electric Power Communication Co., LtdChangchun, China
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Sun, Xiaofu
 e 
Han Xu

Han Xu

Jilin Information & Telecommunication Company, State Grid Jilin Electric Power Corporation LtdChangchun, China
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Xu, Han
  
17 mar 2025
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Pubblicato online: 17 mar 2025
Ricevuto: 06 nov 2024
Accettato: 19 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0224
Parole chiave
© 2025 Weijia Su et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Overall system structure
Overall system structure

Figure 2.

Network security evaluation model
Network security evaluation model

Figure 3.

The prediction accuracy of several discrete algorithms is compared
The prediction accuracy of several discrete algorithms is compared

Data set information description

Data set Number of continuous properties Class number Instance number
Iris 6 3 132
Breast 11 3 631
Wine 9 4 241
Sonar 68 5 213

The simplest decision information table

Rule Condition Decision D
a b c d e f g h i
1 1 2 3 - 2 - 3 1 - 1
2 1 - 2 - - 3 - - 1 1
3 - 3 2 - - 1 - 2 3 3
4 - - - 2 3 - - 1 3 2
5 3 - 2 - 1 - - 2 1 2
6 3 - 1 - - 2 2 - 2 3
7 - 3 - - - 1 - 3 2 3
8 2 - 1 - - 2 3 1 2 2
9 1 - - 3 - - - 3 - 1
10 3 2 - 1 - 2 3 3 - 3

Rough set evaluation results

Sample number Actual output Expected output Relative error/% Threat level
91 0.887 0.865 2.54 Height
92 0.843 0.828 1.81 Medium
93 0.869 0.859 1.16 Height
94 0.826 0.817 1.1 Height
95 0.524 0.531 1.32 Low
96 0.324 0.325 0.31 Height
97 0.334 0.339 1.47 Height
98 0.861 0.875 1.6 Medium
99 0.212 0.207 2.42 Low
100 0.403 0.409 1.47 Low

BP neural network evaluation results

Sample number Actual output Expected output Relative error/% Threat level
91 0.875 0.865 1.16 Height
92 0.837 0.828 1.09 Medium
93 0.885 0.859 3.03 Height
94 0.862 0.817 5.51 Height
95 0.551 0.531 3.77 Low
96 0.375 0.325 15.38 Height
97 0.343 0.339 1.18 Height
98 0.837 0.875 4.34 Medium
99 0.201 0.207 2.9 Low
100 0.365 0.409 10.76 Low
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