Integrating user demographic parameters for mouse behavioral biometric-based assessment fraud detection in online education platforms
Abstract Online education systems have gained immense popularity due to their ubiquity, flexibility, openness, and accessibility. This has led many higher education institutions to incorporate online courses as part of blended or fully online learning. However, online assessment fraud remains a crit...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | EURASIP Journal on Information Security |
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| Online Access: | https://doi.org/10.1186/s13635-025-00207-5 |
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| _version_ | 1849334626835759104 |
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| author | Aditya Subash Insu Song Ickjai Lee Kyungmi Lee |
| author_facet | Aditya Subash Insu Song Ickjai Lee Kyungmi Lee |
| author_sort | Aditya Subash |
| collection | DOAJ |
| description | Abstract Online education systems have gained immense popularity due to their ubiquity, flexibility, openness, and accessibility. This has led many higher education institutions to incorporate online courses as part of blended or fully online learning. However, online assessment fraud remains a critical challenge. Conventional assessment fraud detection methods are often one-time, non-repudiable, invasive, expensive, and susceptible to spoofing. Even some advanced systems based on behavioral biometrics report comparatively lower accuracy, underscoring the ongoing challenge of achieving reliable user authentication. Furthermore, few research studies focus on behavioral biometric-based assessment fraud detection in online education platforms. To address these gaps, we introduce the UserID.AGE.GEN framework, which implements a cross-referencing fusion algorithm that integrates user demographic parameters, including age and gender, with mouse behavioral biometrics for user identity verification for online assessment fraud. Additionally, we collect novel task-specific data for our evaluation. Experimental results demonstrate that our method achieves promising results compared to some existing models, highlighting its strong performance and promising potential for broader application and future enhancement. A notable limitation of the proposed model is that it has not yet been evaluated using significantly larger external datasets, which may affect the generalizability of the results. Our evaluation was conducted using internally collected datasets. Additionally, the model has not been tested in real-world settings such as online education platforms, which may limit insights into its practical deployment. |
| format | Article |
| id | doaj-art-9349b8fefd62492896165f5035324d9e |
| institution | Kabale University |
| issn | 2510-523X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Information Security |
| spelling | doaj-art-9349b8fefd62492896165f5035324d9e2025-08-20T03:45:31ZengSpringerOpenEURASIP Journal on Information Security2510-523X2025-07-012025111710.1186/s13635-025-00207-5Integrating user demographic parameters for mouse behavioral biometric-based assessment fraud detection in online education platformsAditya Subash0Insu Song1Ickjai Lee2Kyungmi Lee3College of Science and Engineering, James Cook UniversityCollege of Science and Engineering, James Cook UniversityCollege of Science and Engineering, James Cook UniversityCollege of Science and Engineering, James Cook UniversityAbstract Online education systems have gained immense popularity due to their ubiquity, flexibility, openness, and accessibility. This has led many higher education institutions to incorporate online courses as part of blended or fully online learning. However, online assessment fraud remains a critical challenge. Conventional assessment fraud detection methods are often one-time, non-repudiable, invasive, expensive, and susceptible to spoofing. Even some advanced systems based on behavioral biometrics report comparatively lower accuracy, underscoring the ongoing challenge of achieving reliable user authentication. Furthermore, few research studies focus on behavioral biometric-based assessment fraud detection in online education platforms. To address these gaps, we introduce the UserID.AGE.GEN framework, which implements a cross-referencing fusion algorithm that integrates user demographic parameters, including age and gender, with mouse behavioral biometrics for user identity verification for online assessment fraud. Additionally, we collect novel task-specific data for our evaluation. Experimental results demonstrate that our method achieves promising results compared to some existing models, highlighting its strong performance and promising potential for broader application and future enhancement. A notable limitation of the proposed model is that it has not yet been evaluated using significantly larger external datasets, which may affect the generalizability of the results. Our evaluation was conducted using internally collected datasets. Additionally, the model has not been tested in real-world settings such as online education platforms, which may limit insights into its practical deployment.https://doi.org/10.1186/s13635-025-00207-5Online assessment fraud detectionHigh false positive ratesUserID.AGE.GEN frameworkCross-referencing fusion algorithmOnline education platforms |
| spellingShingle | Aditya Subash Insu Song Ickjai Lee Kyungmi Lee Integrating user demographic parameters for mouse behavioral biometric-based assessment fraud detection in online education platforms EURASIP Journal on Information Security Online assessment fraud detection High false positive rates UserID.AGE.GEN framework Cross-referencing fusion algorithm Online education platforms |
| title | Integrating user demographic parameters for mouse behavioral biometric-based assessment fraud detection in online education platforms |
| title_full | Integrating user demographic parameters for mouse behavioral biometric-based assessment fraud detection in online education platforms |
| title_fullStr | Integrating user demographic parameters for mouse behavioral biometric-based assessment fraud detection in online education platforms |
| title_full_unstemmed | Integrating user demographic parameters for mouse behavioral biometric-based assessment fraud detection in online education platforms |
| title_short | Integrating user demographic parameters for mouse behavioral biometric-based assessment fraud detection in online education platforms |
| title_sort | integrating user demographic parameters for mouse behavioral biometric based assessment fraud detection in online education platforms |
| topic | Online assessment fraud detection High false positive rates UserID.AGE.GEN framework Cross-referencing fusion algorithm Online education platforms |
| url | https://doi.org/10.1186/s13635-025-00207-5 |
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