AI-assisted security testing in 5G networks for teaching cybersecurity with GitHub Copilot.
Saved in:
| Title: | AI-assisted security testing in 5G networks for teaching cybersecurity with GitHub Copilot. |
|---|---|
| Authors: | Tsai, Yi-Hsueh1,2 yihsuehtsai@g.ntu.edu.tw, Chen, Shiang-Jiun1 annette@ntut.edu.tw, Hsiao, Li-Cheng2,3 s1091750@mail.yzu.edu.tw, Chen, Chun-Yang1 t113c53017@ntut.edu.tw, Wang, Shao-Lei1 solomon12354@gmail.com, Wu, Chia-Jung1 stanny6427@gmail.com |
| Source: | Educational Technology & Society. Apr2026, Vol. 29 Issue 2, p46-68. 23p. |
| Subject Terms: | *Science education, 5G networks, Code generators |
| Abstract: | The increasing complexity of 5G networks introduces significant security risks, particularly within the User Plane Function (UPF). The N4 interface and Packet Forwarding Control Protocol (PFCP) are key targets for session hijacking, misconfigured policies, and Distributed Denial of Service (DDoS) attacks. However, teaching 5G security testing remains challenging due to its technical complexity. This paper proposes an AI-assisted approach that integrates GitHub Copilot into cybersecurity education. Students use Copilot to automate testing tasks, simulate attacks, and analyze N4 vulnerabilities. Experimental results show that this approach enhances vulnerability detection, coding efficiency, and cybersecurity skills. AI-generated code bridges the gap between theory and practice, supporting hands-on learning. Our findings confirm that incorporating AI tools fosters skill development, critical thinking, and real-world testing ability, advancing 5G security education. In addition to demonstrating the educational benefits of AI-assisted tools, this study also acknowledges potential risks associated with automated code generation. To ensure secure and responsible use, the framework emphasizes manual validation of AI-generated scripts and the incorporation of secure coding practices. This highlights the need for cybersecurity education to strike a balance between efficiency and critical evaluation, as well as ethical awareness, when integrating AI into 5G security training. [ABSTRACT FROM AUTHOR] |
| Copyright of Educational Technology & Society is the property of International Forum of Educational Technology & Society (IFETS) 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.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: ehh DbLabel: Education Research Complete An: 192828621 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: AI-assisted security testing in 5G networks for teaching cybersecurity with GitHub Copilot. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tsai%2C+Yi-Hsueh%22">Tsai, Yi-Hsueh</searchLink><relatesTo>1,2</relatesTo><i> yihsuehtsai@g.ntu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Shiang-Jiun%22">Chen, Shiang-Jiun</searchLink><relatesTo>1</relatesTo><i> annette@ntut.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Hsiao%2C+Li-Cheng%22">Hsiao, Li-Cheng</searchLink><relatesTo>2,3</relatesTo><i> s1091750@mail.yzu.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Chun-Yang%22">Chen, Chun-Yang</searchLink><relatesTo>1</relatesTo><i> t113c53017@ntut.edu.tw</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Shao-Lei%22">Wang, Shao-Lei</searchLink><relatesTo>1</relatesTo><i> solomon12354@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Chia-Jung%22">Wu, Chia-Jung</searchLink><relatesTo>1</relatesTo><i> stanny6427@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Educational+Technology+%26+Society%22">Educational Technology & Society</searchLink>. Apr2026, Vol. 29 Issue 2, p46-68. 23p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Science+education%22">Science education</searchLink><br /><searchLink fieldCode="DE" term="%225G+networks%22">5G networks</searchLink><br /><searchLink fieldCode="DE" term="%22Code+generators%22">Code generators</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The increasing complexity of 5G networks introduces significant security risks, particularly within the User Plane Function (UPF). The N4 interface and Packet Forwarding Control Protocol (PFCP) are key targets for session hijacking, misconfigured policies, and Distributed Denial of Service (DDoS) attacks. However, teaching 5G security testing remains challenging due to its technical complexity. This paper proposes an AI-assisted approach that integrates GitHub Copilot into cybersecurity education. Students use Copilot to automate testing tasks, simulate attacks, and analyze N4 vulnerabilities. Experimental results show that this approach enhances vulnerability detection, coding efficiency, and cybersecurity skills. AI-generated code bridges the gap between theory and practice, supporting hands-on learning. Our findings confirm that incorporating AI tools fosters skill development, critical thinking, and real-world testing ability, advancing 5G security education. In addition to demonstrating the educational benefits of AI-assisted tools, this study also acknowledges potential risks associated with automated code generation. To ensure secure and responsible use, the framework emphasizes manual validation of AI-generated scripts and the incorporation of secure coding practices. This highlights the need for cybersecurity education to strike a balance between efficiency and critical evaluation, as well as ethical awareness, when integrating AI into 5G security training. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Educational Technology & Society is the property of International Forum of Educational Technology & Society (IFETS) 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=192828621 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.30191/ETS.202604_29(2).RP03 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 46 Subjects: – SubjectFull: Science education Type: general – SubjectFull: 5G networks Type: general – SubjectFull: Code generators Type: general Titles: – TitleFull: AI-assisted security testing in 5G networks for teaching cybersecurity with GitHub Copilot. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tsai, Yi-Hsueh – PersonEntity: Name: NameFull: Chen, Shiang-Jiun – PersonEntity: Name: NameFull: Hsiao, Li-Cheng – PersonEntity: Name: NameFull: Chen, Chun-Yang – PersonEntity: Name: NameFull: Wang, Shao-Lei – PersonEntity: Name: NameFull: Wu, Chia-Jung IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 11763647 Numbering: – Type: volume Value: 29 – Type: issue Value: 2 Titles: – TitleFull: Educational Technology & Society Type: main |
| ResultId | 1 |
