Application of big data technology in enterprise information security management
Abstract This study aims to explore the application value of big data technology (BDT) in enterprise information security (EIS). Its goal is to develop a risk prediction model based on big data analysis to enhance the information security protection capability of enterprises. A big data analysis sys...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85403-6 |
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| _version_ | 1841544645211848704 |
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| author | Ping Li Limin Zhang |
| author_facet | Ping Li Limin Zhang |
| author_sort | Ping Li |
| collection | DOAJ |
| description | Abstract This study aims to explore the application value of big data technology (BDT) in enterprise information security (EIS). Its goal is to develop a risk prediction model based on big data analysis to enhance the information security protection capability of enterprises. A big data analysis system that can monitor and intelligently identify potential security risks in real-time is constructed by designing complex network analysis algorithms and machine learning models. For different types of security threats, the system uses feature engineering and model training processes to extract key risk indicators and optimize model prediction performance. The experimental results show that the constructed risk prediction model has excellent performance on the test set, and its Area Under the Curve reaches 0.95, indicating that the model has good differentiation ability and high prediction accuracy. In addition, in the multi-class risk identification task, the model achieves an average precision of 0.87. Compared with the traditional method, it has remarkably improved the early warning accuracy and response speed of enterprises to various information security incidents. Therefore, this study confirms the effectiveness and feasibility of applying BDT to EIS risk management, and the successfully constructed prediction model provides strong technical support for EIS protection. |
| format | Article |
| id | doaj-art-21faa37164fe4679a0fe61d33e7a1fa1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-21faa37164fe4679a0fe61d33e7a1fa12025-01-12T12:22:43ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-025-85403-6Application of big data technology in enterprise information security managementPing Li0Limin Zhang1School of Information and Mechatronic Engineering, Hunan International Economics UniversityCollege of Electrical and Information Engineering, Hunan Institute of Traffic EngineeringAbstract This study aims to explore the application value of big data technology (BDT) in enterprise information security (EIS). Its goal is to develop a risk prediction model based on big data analysis to enhance the information security protection capability of enterprises. A big data analysis system that can monitor and intelligently identify potential security risks in real-time is constructed by designing complex network analysis algorithms and machine learning models. For different types of security threats, the system uses feature engineering and model training processes to extract key risk indicators and optimize model prediction performance. The experimental results show that the constructed risk prediction model has excellent performance on the test set, and its Area Under the Curve reaches 0.95, indicating that the model has good differentiation ability and high prediction accuracy. In addition, in the multi-class risk identification task, the model achieves an average precision of 0.87. Compared with the traditional method, it has remarkably improved the early warning accuracy and response speed of enterprises to various information security incidents. Therefore, this study confirms the effectiveness and feasibility of applying BDT to EIS risk management, and the successfully constructed prediction model provides strong technical support for EIS protection.https://doi.org/10.1038/s41598-025-85403-6Big data technologyEnterprise information securityRisk prediction modelInformation security |
| spellingShingle | Ping Li Limin Zhang Application of big data technology in enterprise information security management Scientific Reports Big data technology Enterprise information security Risk prediction model Information security |
| title | Application of big data technology in enterprise information security management |
| title_full | Application of big data technology in enterprise information security management |
| title_fullStr | Application of big data technology in enterprise information security management |
| title_full_unstemmed | Application of big data technology in enterprise information security management |
| title_short | Application of big data technology in enterprise information security management |
| title_sort | application of big data technology in enterprise information security management |
| topic | Big data technology Enterprise information security Risk prediction model Information security |
| url | https://doi.org/10.1038/s41598-025-85403-6 |
| work_keys_str_mv | AT pingli applicationofbigdatatechnologyinenterpriseinformationsecuritymanagement AT liminzhang applicationofbigdatatechnologyinenterpriseinformationsecuritymanagement |