Subash, Aditya, Song, Insu, Lee, Ickjai, and Lee, Kyungmi (2024) Mouse Dynamics-Based Online Fraud Detection System for Online Education Platforms. In: Lecture Notes in Networks and Systems (1003) pp. 257-269. From: ICICT 2024: 9th International Congress on Information and Communication Technology, 19-22 February 2024, London, UK.

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Abstract

Over the past decade, the online education sector has gained significant popularity causing a major shift from traditional face-to-face classes to online education. This shift has left the education sector with major vulnerabilities. Specifically online fraud, which includes identity theft and online assessment fraud. Conventional methods used for online fraud detection are one-time, invasive, and expensive. Behavioral biometric authentication systems based on mouse dynamics have emerged as a secure and inexpensive alternative to conventional methods due to their ability to continuously track user identity non-invasively. However, there are no studies on mouse behavioral biometric-based continuous user identification, for online fraud detection, in online education platforms. Furthermore, there is also a need to identify a better combination of features that yield satisfactory performance (>80% in accuracy, precision, and recall) for this specific application. Existing research in the field still relies on typical machine learning methods despite major developments made in deep learning. To solve the identified problems, we first identify a unique combination of mouse behavior features and collect application-oriented mouse behavior data. We then investigate the efficacy of the features using both deep learning and typical machine learning methods. Experimental results demonstrate that the identified feature set, termed as user behavior-centric feature set, not only yields satisfactory performance but is reliable and scalable to significantly larger datasets.

Item ID:	84919
Item Type:	Conference Item (Research - E1)
ISBN:	978-981-97-3301-9
Copyright Information:	© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. X.-S. Yang et al. (eds.), Proceedings of Ninth International Congress on Information and Communication Technology, Lecture Notes in Networks and Systems 1003.
Date Deposited:	18 Mar 2025 22:03
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460402 Data and information privacy @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 50%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220499 Information systems, technologies and services not elsewhere classified @ 50%
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