Academic Journal
AN AI-DRIVEN DIGITAL TWIN FRAMEWORK LEVERAGING ONTOLOGIES, INTELLIGENT DATA MANAGEMENT, AND SIMULATION FOR SECURITY AND RESILIENCE IN 6G NETWORKS.
| Τίτλος: | AN AI-DRIVEN DIGITAL TWIN FRAMEWORK LEVERAGING ONTOLOGIES, INTELLIGENT DATA MANAGEMENT, AND SIMULATION FOR SECURITY AND RESILIENCE IN 6G NETWORKS. |
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| Συγγραφείς: | Karunakaran, P., Bostani, Ali, Gulom, Salomov, Kumar, S. Shantha, Manimala, V., Velmurugan, T., Praveenkumar, R. |
| Πηγή: | Archives for Technical Sciences / Arhiv za Tehnicke Nauke; 2025, Issue 34, p951-964, 14p |
| Θεματικοί όροι: | 6G networks, Digital twin, Automatic control systems, Knowledge representation (Information theory), Security management, Electronic data processing, Disaster resilience, Virtual reality |
| Περίληψη: | The 6G wireless networks will be used in autonomous systems, extended reality, digital healthcare, and large-scale cyber-physical infrastructures, making it possible to provide applications they previously did not know to be intelligent and ultra-reliable communication. Nevertheless, 6G networks are quite complicated and heterogeneous, which are challenging to Security, resilience, and autonomous management and cannot be addressed successfully with common reactive techniques. In an attempt to solve these issues, this paper presents an AI-based Digital Twin architecture, which is characterized by ontology-based knowledge representation, intelligent data management, and simulation-based analysis to enhance Security and resilience. The suggested structure forms an ever-in-step virtual representation of the real 6G network that will allow real-time tracking, predictive analytics, and proactive decision-making. Integrating AI models with the framework helps identify intrusions and detect anomalies at 98 percent accuracy, and also shortens the response time by 40 percent. Also, the multi-level simulated environment analyzes the cyber-attack cases and action plans prior to deployment, making the system resilient, with the recovery time of failures cut by 30 percent. This is a closed-loop automation that is driven by statistical learning and rational decision-making based on knowledge, resulting in higher situational awareness, shorter response time, and higher network availability. The continual learning ability of the Digital Twin that keeps models updated with live network information results in its flexibility to the dynamic and heterogeneous nature of the future 6G networks. The solution provides a scalable, secure, and resilient base of autonomous 6G networks, which can help achieve the creation of trustworthy and intelligent communication systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Archives for Technical Sciences / Arhiv za Tehnicke Nauke is the property of Archives for Technical Science / Arhiv za tehnicke nauke and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 192297312 RelevancyScore: 1041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1040.81262207031 |
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| Items | – Name: Title Label: Title Group: Ti Data: AN AI-DRIVEN DIGITAL TWIN FRAMEWORK LEVERAGING ONTOLOGIES, INTELLIGENT DATA MANAGEMENT, AND SIMULATION FOR SECURITY AND RESILIENCE IN 6G NETWORKS. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Karunakaran%2C+P%2E%22">Karunakaran, P.</searchLink><br /><searchLink fieldCode="AR" term="%22Bostani%2C+Ali%22">Bostani, Ali</searchLink><br /><searchLink fieldCode="AR" term="%22Gulom%2C+Salomov%22">Gulom, Salomov</searchLink><br /><searchLink fieldCode="AR" term="%22Kumar%2C+S%2E+Shantha%22">Kumar, S. Shantha</searchLink><br /><searchLink fieldCode="AR" term="%22Manimala%2C+V%2E%22">Manimala, V.</searchLink><br /><searchLink fieldCode="AR" term="%22Velmurugan%2C+T%2E%22">Velmurugan, T.</searchLink><br /><searchLink fieldCode="AR" term="%22Praveenkumar%2C+R%2E%22">Praveenkumar, R.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Archives for Technical Sciences / Arhiv za Tehnicke Nauke; 2025, Issue 34, p951-964, 14p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%226G+networks%22">6G networks</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+control+systems%22">Automatic control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+representation+%28Information+theory%29%22">Knowledge representation (Information theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Security+management%22">Security management</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Disaster+resilience%22">Disaster resilience</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+reality%22">Virtual reality</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The 6G wireless networks will be used in autonomous systems, extended reality, digital healthcare, and large-scale cyber-physical infrastructures, making it possible to provide applications they previously did not know to be intelligent and ultra-reliable communication. Nevertheless, 6G networks are quite complicated and heterogeneous, which are challenging to Security, resilience, and autonomous management and cannot be addressed successfully with common reactive techniques. In an attempt to solve these issues, this paper presents an AI-based Digital Twin architecture, which is characterized by ontology-based knowledge representation, intelligent data management, and simulation-based analysis to enhance Security and resilience. The suggested structure forms an ever-in-step virtual representation of the real 6G network that will allow real-time tracking, predictive analytics, and proactive decision-making. Integrating AI models with the framework helps identify intrusions and detect anomalies at 98 percent accuracy, and also shortens the response time by 40 percent. Also, the multi-level simulated environment analyzes the cyber-attack cases and action plans prior to deployment, making the system resilient, with the recovery time of failures cut by 30 percent. This is a closed-loop automation that is driven by statistical learning and rational decision-making based on knowledge, resulting in higher situational awareness, shorter response time, and higher network availability. The continual learning ability of the Digital Twin that keeps models updated with live network information results in its flexibility to the dynamic and heterogeneous nature of the future 6G networks. The solution provides a scalable, secure, and resilient base of autonomous 6G networks, which can help achieve the creation of trustworthy and intelligent communication systems. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Archives for Technical Sciences / Arhiv za Tehnicke Nauke is the property of Archives for Technical Science / Arhiv za tehnicke nauke and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.70102/afts.2025.1834.951 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 951 Subjects: – SubjectFull: 6G networks Type: general – SubjectFull: Digital twin Type: general – SubjectFull: Automatic control systems Type: general – SubjectFull: Knowledge representation (Information theory) Type: general – SubjectFull: Security management Type: general – SubjectFull: Electronic data processing Type: general – SubjectFull: Disaster resilience Type: general – SubjectFull: Virtual reality Type: general Titles: – TitleFull: AN AI-DRIVEN DIGITAL TWIN FRAMEWORK LEVERAGING ONTOLOGIES, INTELLIGENT DATA MANAGEMENT, AND SIMULATION FOR SECURITY AND RESILIENCE IN 6G NETWORKS. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Karunakaran, P. – PersonEntity: Name: NameFull: Bostani, Ali – PersonEntity: Name: NameFull: Gulom, Salomov – PersonEntity: Name: NameFull: Kumar, S. Shantha – PersonEntity: Name: NameFull: Manimala, V. – PersonEntity: Name: NameFull: Velmurugan, T. – PersonEntity: Name: NameFull: Praveenkumar, R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 18404855 Numbering: – Type: issue Value: 34 Titles: – TitleFull: Archives for Technical Sciences / Arhiv za Tehnicke Nauke Type: main |
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