A Performance Improvement of Mouse Data Protection Using CTGAN: A Method for Generation of Realistic Synthetic Mouse Data 


Vol. 14,  No. 11, pp. 925-934, Nov.  2025
https://doi.org/10.3745/TKIPS.2025.14.11.925


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  Abstract

Online services delivered via computer systems require reliable user authentication for secure, non-face-to-face access. Historically, password-based user authentication has been widely used, but it remains vulnerable to credential-theft attacks such as keyloggers. Image-based user authentication methods were introduced to mitigate password exposure; however, these methods can be subverted by attacks that steal mouse input data via the WM_INPUT message, thereby neutralizing their security benefits. To counter such threats, defense techniques that inject decoy(synthetic) mouse data have been proposed. Recent advances in machine learning, however have enabled attackers to distinguish between synthetic and real mouse data with up to 99% accuracy, exposing a critical vulnerability in these defenses. To address machine learning-driven attacks on mouse data protection, prior research has applied Conditional Tabular Generative Adversarial Networks(CTGAN) to synthesize realistic decoy mouse data, demonstrating reductions in attack success rate of up to 37%. In this paper, we proposed enhancements to CTGAN-based mouse data defenses aimed at further improving protection effectiveness. Specifically, we investigate the effects of data normalization strategies, systematic hyperparameter tuning, and epoch configuration on the fidelity of generated decoy mouse data, and we introduce an optimized generation pipeline for producing high-quality synthetic mouse data. We evaluate defense performance across different data-processing and synthesis frequencies, measuring relative changes in attack success rates. Experimental results show that our proposed approach lowest attack success rates by up to 42%, representing a 5% absolute improvement over prior CTGAN-based defenses. Furthermore, our method achieves approximately and 18% improvement in average defensive performance compared to prior work. These findings demonstrate that the proposed CTGAN tuning and processing techniques materially strengthen protection of image-based user authentication data against modern machine learning-based attacks.

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[IEEE Style]

J. Kim and K. Lee, "A Performance Improvement of Mouse Data Protection Using CTGAN: A Method for Generation of Realistic Synthetic Mouse Data," The Transactions of the Korea Information Processing Society, vol. 14, no. 11, pp. 925-934, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.11.925.

[ACM Style]

Jinwook Kim and Kyungroul Lee. 2025. A Performance Improvement of Mouse Data Protection Using CTGAN: A Method for Generation of Realistic Synthetic Mouse Data. The Transactions of the Korea Information Processing Society, 14, 11, (2025), 925-934. DOI: https://doi.org/10.3745/TKIPS.2025.14.11.925.

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