## 2 Background and Related Work
[PERSON] and [PERSON] (2020) [22] proposed a new one-dimensional convolutional network architecture using two datasets: the Balabit public dataset for performance evaluation [23], and the DFL dataset for transfer learning. (The DFL dataset was used to initialize the weights of our models). To avoid overfitting, they used the sigmoid activation function and a dropout layer with 0.15 probability. Moreover, their 1D-CNN model was trained in Keras using the Adam optimizer (learning rate: 0.002, decay: 0.0001, loss function: binary cross-entropy). They segmented the mouse dynamics data into fixed-size blocks and made two types of measurements: measurements using 300 blocks from each user (class-balanced measurement), and measurements using all blocks of data from each user (class-imbalanced measurement). They evaluated the model using three scenarios: (i) PLAIN models, trained from scratch using the training data from the Balabit dataset; (ii) TRANSFER1 models that use transfer learning, where the models were pre-trained on the DFL dataset; and (iii) TRANSFER2 models that were initialized with transfer learning, and then the weights were updated using the training data from the Balabit dataset. These were the results for number of blocks (300): PLAIN = 0.63, TRANSFER1 = 0.50, and TRANSFER2 = 0.66. These were the results for number of blocks (all): PLAIN = 0.55, TRANSFER1 = 0.34, and TRANSFER2 = 0.62. The Balabit public dataset was used in this study; it contains the mouse data from only 10 users, which may not be sufficient to produce a trustworthy and secure user model based on mouse dynamics. [PERSON] et al. (2019) [24] proposed different strategies that a potential attacker could use to carry out synthetically generated adversarial samples by using approaches based on imitation, a surrogate, or statistics. On the basis of the results of their experiments,they concluded that attacks based on neural networks perform better than statistics-based attacks. They discussed how the generation of mouse sequences is a difficult task to handle, and consequently the authors proposed that adversarial attacks have their flaws when carried out. The authors also elaborated on ways in which the robustness of these authentication models can be adversely affected, even when tested in a realistic way. In the latter half of the article, the authors showed the mechanism for accruing results from different experiments discussed in this paper. At the end of this article, they provide an overview of the extension of their surrogate-based attack approach. [PERSON] and [PERSON] (2018) proposed a system of empirical biometric-based study for user identification using different neural networks in the online game League of Legends. The results of their experiments showed how different neural networks behave with the League of Legends biometric data and databases. The RBF and Bayesian networks indicated that it is possible to improve results by collecting the samples more often, despite the cost of processing. At the end, the authors state that future work can use a route where the separations between early-game, mid-game, and late-game samples are analyzed; this approach examines the user profile at each level as the same player carries out the game from beginning to end. Summing up this article, the authors mentioned that the traditional way of obtaining user verification by simply using email is not user-friendly because many AI-based features can be compromised if that approach is used. The amount of data that was collected was not sufficient to allow experiments to verify if a user is themselves attacking the account-sharing problem more precisely. Table 1 provides a concise summary of the most important 10 studies on user authentication using mouse dynamics. The first column in each entry provides the source of the study. The remaining columns provide more information as given below:  
* **Number of users:** The number of users that participated. * **Data period:** The time of gathering the user's information.