Integrating user demographic parameters for mouse behavioral biometric-based assessment fraud detection in online education platforms
Subash, Aditya, Song, Insu, Lee, Ickjai, and Lee, Kyungmi (2025) Integrating user demographic parameters for mouse behavioral biometric-based assessment fraud detection in online education platforms. Eurasip Journal on Information Security, 2025. 21.
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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.
| Item ID: | 87690 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 2510-523X |
| Keywords: | Cross-referencing fusion algorithm, High false positive rates, Online assessment fraud detection, Online education platforms, UserID.AGE.GEN framework |
| Copyright Information: | © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. |
| Date Deposited: | 27 Oct 2025 05:42 |
| FoR Codes: | 46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460407 System and network security @ 100% |
| SEO Codes: | 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220405 Cybersecurity @ 100% |
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