AgeGen Bio Track: Continuous Mouse Behavioral Biometrics-Based Age and Gender Profiling in Online Education Platforms

Mouse behavioral biometric-based authentication systems have attracted significant attention as they are considered a more secure alternative to conventional online assessment fraud detection systems. This is attributed to their ability to continuously authenticate users non-intrusively by analyzing their distinctive mouse operating behavior. Most behavioral biometric-based research studies focus on predicting user identity as the primary objective for online assessment fraud detection. However, they do not consider predicting other user-centric parameters like age and gender. Furthermore, there is a need to identify the best segmentation approach and mouse behavior feature set for age and gender classification. We propose the AgeGen Bio track system, a continuous mouse behavioral biometric-based age and gender tracking system for online education platforms. To accomplish this, we first collect novel mouse behavior data with user demographic information. We then evaluate the efficacy of different segmentation approaches, feature sets, and machine learning models for age and gender classification. Experimental results show that the random forest algorithm paired with the three mouse-movement segmentation approach and user characteristic feature set are the best approaches that need to be incorporated into the system, as they achieved promising results.

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ICAART 2025: 17th International Conference on Agents and Artificial Intelligence

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978-989-758-737-5

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