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HybridTouch: A Robust Framework for Continuous User Authentication by GAN-Augmented Behavioral Biometrics on Mobile Devices | ||
| Journal of Information Technology Management | ||
| دوره 17، Special Issue on SI: Intelligent Security and Management، 2025، صفحه 108-129 اصل مقاله (1.51 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2025.102924 | ||
| نویسندگان | ||
| Mahendra Kumar Jangir1؛ Karan Singh* 1؛ Tayyab Khan2 | ||
| 1Jawaharlal Nehru University, New Delhi, India. | ||
| 2Assistant Prof., Department of Computer Science and Engineering, Indian Institute of Information Technology, Sonipat, IIIT Sonepat, India. | ||
| چکیده | ||
| With an increasing reliance on mobile devices, continuous and assured user authentication is essential to protect sensitive personal data and digital interactions from unwanted access. Based on this background, this research proposed the development of the HybridTouch framework for smartphone-based continuous and passive user authentication. The proposed HybridTouch combines Convolutional Neural Networks for spatial feature extraction and Gated Recurrent Units for temporal sequence analysis. It uses accelerometer, gyroscope, and touch data to take advantage of the unique behavioral patterns captured by it. Innovative preprocessing techniques have been incorporated into the proposed approach: Discrete Wavelet Transform is used for signal denoising, and Variable-Length Adaptive Temporal windowing is used for segmentation based on signal entropy to enhance feature representation. To eliminate the data scarcity limitation, Generative Adversarial Networks were used to synthesize realistic behavioral data that considerably augmented the dataset and enhanced model generalization capability. Extensive experiments conducted on the Hand Movement, Orientation, and Grasp (HMOG) dataset showed that the proposed HybridTouch achieved excellent results with authentication accuracy up to 98.8% with real data, growing up to 99% with GAN-augmented data. The hybrid model further has an equal error rate of 1.4% on real data and 1.25% on synthetic data, which is better than any other models currently present (Sağbas et al., 2024; Siddiqui et al., 2022; Abuhamad et al., 2020) and all implementations of standalone convolutional neural networks and gated recurrent units. | ||
| کلیدواژهها | ||
| Mobile Authentication؛ Touch Dynamics؛ Deep Learning؛ Smartphone Sensors؛ Convolutional Neural Networks | ||
| مراجع | ||
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