## 2 Related Work
However, their approach yielded values of 19 and 21% for FRR and FAR, respectively, which indicate a relatively low authentication performance. Ref. [9] proposed a gesture-based user authentication scheme (GEAT) that uses a multitouch gesture on a touchscreen. They obtained seven features that were derived from velocity, acceleration, and touch stroke. Subsequently, they preprocessed the collected sequential sensor data by using the root mean-squared error (RMSE). Finally, they used the radialbasis function-based support vector distribution estimation for the user-authentication model. The experimental results showed values ranging from 0.94 to 0.96 in terms of area under receiver operating characteristic (AUROC) in 10 gestures. Furthermore, up to 5% equal error rate (EER) was observed when they used 10 data samples. Although they experimented in a real-world situation, their method had several drawbacks. First, they used a statistical handcrafted feature derived from sequential data, which can lead to loss of behavioral characteristics in sequential data. Second, theirs is not an end-to-end authentication method that uses raw sequential data but uses handcrafted features instead. Ref. [8] proposed an enhanced PIN-based authentication method. They built an authentication model that used Gaussian estimation, z-score, and standard deviation drift using the collected n-graph-based time and touch features. According to the experimental results, the proposed method yielded 6.81 and 5.59% average EER, while using 50 and 150 16-digit data samples, respectively. Therefore, low authentication performance is a limitation of their study. Ref. [28] proposed keystroke dynamics-based mobile user authentication. They used accelerator and time-based features transformed by a di-graph and tri-graph. However, their method yielded values of 40 and 7% for FRR and FAR, respectively, which indicate low authentication performance. Ref. [13] proposed a two-stage drawing PIN-based authentication that comprises the following steps. In the first step, they built a digit classifier using k-nearest neighbors (k-NN) to recognize the predefined 4-digit PIN. The second step comprises a template match-based user authentication using behavioral biometrics, such as the coordinate, pressure, and size of the touch area. This can mitigate SSA, which is an inherent drawback of the PIN method. Their method yielded an average EER of 4.84% when an unauthorized user knew the predefined 4-digit PIN, which is commonly assumed in a PIN attack scenario. However, for authentication in the imitation scenario, in which an imposter knows both the predefined PIN and the hand movement, the EER increased to 14.11% on average, which is relatively lower than in the PIN attack scenario. Although they experimented in a real-world scenario, their study had several drawbacks. First, if the PIN numbers were misclassified in the first step, then an authentication failure occurred. Second, they used a median-based similarity score for transforming raw sequential data. This can lead to loss of behavioral characteristics in raw sequential data. Third, their method did not provide sufficiently high authentication performance in the imitation attack scenario. The previous studies on human behavioral biometric-based authentication using a smartphone touchscreen had several drawbacks. First, their methods required a multistage user-authentication process. In other words, they used handcrafted features instead of raw sequential data, thereby causing loss of behavioral characteristics in the raw sequence data. Second, few of these studies are based on deep neural network models that yield excellent performance for sequential data. Third, their methods produced relatively low authentication performance during threat scenarios, such as SSA. Therefore, we propose the DDP method, which is a designed end-to-end authentication model using raw sequential data instead of handcrafted features. It can mitigate the loss of behavioral characteristics in raw sequential data.