Graph-based two-level indicator system construction method for smart city information security risk assessment
The rapid development of urban informatization has led to a deep integration of advanced information technology into urban life. Many decision-makers are starting to alleviate the adverse effects of this informatization process through risk assessment. However, existing methods cannot effectively an...
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| Language: | English |
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AIMS Press
2024-08-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2024237 |
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| author | Li Yang Kai Zou Yuxuan Zou |
| author_facet | Li Yang Kai Zou Yuxuan Zou |
| author_sort | Li Yang |
| collection | DOAJ |
| description | The rapid development of urban informatization has led to a deep integration of advanced information technology into urban life. Many decision-makers are starting to alleviate the adverse effects of this informatization process through risk assessment. However, existing methods cannot effectively analyze internal and hierarchical relationships because of the excessive number of indicators. Thus, it is necessary to construct an indicator's dependency graph and conduct a comprehensive hierarchical analysis to solve this problem. In this study, we proposed a graph-based two-level indicator system construction method. First, a random forest was used to extract the indicators' dependency graph from missing data. Then, spectral clustering was used to separate the graph and form a functional subgraph. Finally, PageRank was used to calculate the prioritization for each subgraph's indicator, and the two-level indicator system was established. To verify the performance, we took China's 25 smart cities as examples. For the simulation of risk level prediction, we compared our method with some machine learning algorithms, such as ridge regression, Lasso regression, support vector regression, decision trees, and multi-layer perceptron. Results showed that the two-level indicator system is superior to the general indicator system for risk assessment. |
| format | Article |
| id | doaj-art-699f8242a52d42b8b09be1b50c78936f |
| institution | Kabale University |
| issn | 2688-1594 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-699f8242a52d42b8b09be1b50c78936f2025-01-23T07:51:28ZengAIMS PressElectronic Research Archive2688-15942024-08-013285139515610.3934/era.2024237Graph-based two-level indicator system construction method for smart city information security risk assessmentLi Yang0Kai Zou1Yuxuan Zou2School of Public Administration, Xiangtan University, Xiangtan 411105, ChinaSchool of Public Administration, Xiangtan University, Xiangtan 411105, ChinaSchools of Management, Xi'an Jiantong University, Xi'an 710049, ChinaThe rapid development of urban informatization has led to a deep integration of advanced information technology into urban life. Many decision-makers are starting to alleviate the adverse effects of this informatization process through risk assessment. However, existing methods cannot effectively analyze internal and hierarchical relationships because of the excessive number of indicators. Thus, it is necessary to construct an indicator's dependency graph and conduct a comprehensive hierarchical analysis to solve this problem. In this study, we proposed a graph-based two-level indicator system construction method. First, a random forest was used to extract the indicators' dependency graph from missing data. Then, spectral clustering was used to separate the graph and form a functional subgraph. Finally, PageRank was used to calculate the prioritization for each subgraph's indicator, and the two-level indicator system was established. To verify the performance, we took China's 25 smart cities as examples. For the simulation of risk level prediction, we compared our method with some machine learning algorithms, such as ridge regression, Lasso regression, support vector regression, decision trees, and multi-layer perceptron. Results showed that the two-level indicator system is superior to the general indicator system for risk assessment.https://www.aimspress.com/article/doi/10.3934/era.2024237smart cityinformation securityrisk assessmenttwo-level indicator system constructionmachine learning |
| spellingShingle | Li Yang Kai Zou Yuxuan Zou Graph-based two-level indicator system construction method for smart city information security risk assessment Electronic Research Archive smart city information security risk assessment two-level indicator system construction machine learning |
| title | Graph-based two-level indicator system construction method for smart city information security risk assessment |
| title_full | Graph-based two-level indicator system construction method for smart city information security risk assessment |
| title_fullStr | Graph-based two-level indicator system construction method for smart city information security risk assessment |
| title_full_unstemmed | Graph-based two-level indicator system construction method for smart city information security risk assessment |
| title_short | Graph-based two-level indicator system construction method for smart city information security risk assessment |
| title_sort | graph based two level indicator system construction method for smart city information security risk assessment |
| topic | smart city information security risk assessment two-level indicator system construction machine learning |
| url | https://www.aimspress.com/article/doi/10.3934/era.2024237 |
| work_keys_str_mv | AT liyang graphbasedtwolevelindicatorsystemconstructionmethodforsmartcityinformationsecurityriskassessment AT kaizou graphbasedtwolevelindicatorsystemconstructionmethodforsmartcityinformationsecurityriskassessment AT yuxuanzou graphbasedtwolevelindicatorsystemconstructionmethodforsmartcityinformationsecurityriskassessment |