User Authentication Based on Mouse Dynamics Using an Efficient-Net Model

Abstract

As digital threats become increasingly sophisticated, user authentication has become vitally important in cybersecurity. Traditional authentication methods such as passwords are under increasing assault from a range of attacks. Behavioral biometrics, such as mouse dynamics, have the potential to address these attacks in a way that is largely passive and continuous. In this paper, we present a new solution that rests on mouse dynamics behavior together with a lightweight deep learning model inspired by EfficientNet, specifically designed for Behavioral Assessment of Numerical Data (BAND). The SapiMouse dataset, consisting of mouse tracking data from 120 actual users, is harnessed. By applying preprocessing techniques such as Quantile Transformation and Min-Max Encoding, along with encoding, the raw data were prepared for model training. The modified EfficientNet model retains its computational efficiency while also being tailored to work with numerical input. Its structure uses compact convolutions along with compound scaling to capture time-series mouse data discriminative features, lowering the processing burden while maintaining accuracy. Moreover, to stabilize training and enhance generalization, dropout and batch normalization layers were added, ensuring robustness to overfitting, even when using data generated by a model. CGAN’s capacity for class sample synthesis was harnessed towards improving recognition of unused user profiles, resulting in a total of 240 unique classes (120 real + 120 synthetic). The model reached an accuracy of 99.24% for classification and a macro-averaged F1-score of 0.991 on the testing set. An inference time of only 0.2331 seconds per sample, alongside a cumulative training duration of 158.25 seconds, suggests real-time applicability. These findings support the promise of repurposing advanced deep learning models for behavioral biometrics, providing affordable, scalable, and efficient user verification for sensitive security contexts.