## 1 Introduction  
Behavioral biometrics measure and analyze user interactions in the online domain so as to recognize or verify a person's unique identity, with the ultimate goal of providing an imperceptible layer of security to systems and applications [1]. The best-known forms of behavioral biometrics involve the monitoring and analysis of the following modalities: mouse cursor movement, keystroke or voice dynamics, the appearance and speed of signing, etc. The main advantages of mouse movement analysis relative to the other forms of behavioral biometrics include: (a) mouse movement can be monitored in a manner that is entirely unobtrusive for the end user; (b) monitoring of mouse movement does not require the use of additional hardware or software and thus does not incur additional cost; (c) from the perspective of user privacy, sharing mouse dynamics data is far less problematic than sharing keystrokes, signatures or voice data [2]; (d) mouse movement has already proven to be effective, not only in the identification or authentication of end users but also in the process of determining users' age and gender [3], as well as their emotions and work productivity [4]. A number of previous studies on mouse dynamics have looked at the importance of different mouse movement characteristics for the purpose of user identification/authentication,such as hesitation patterns, random and straight movements, etc. [5]. Some of these studies have also looked at the use of different machine learning methods in user identification/authentication systems; however, they often rely only on a limited number of handpicked features extracted from their respective mouse movement datasets. To avoid the pitfalls of manual feature extraction processes, in this study we propose to tackle the problem of mouse trajectory classification by using a deep neural network (convolutional neural network) that utilizes all of the raw mouse movement data. That is, instead of handpicking the most important features for a set of mouse movement trajectories, we let the convolution neural network identify these features in an unsupervised manner. Furthermore, we investigate the use of mouse movement analysis in another important application area--malicious web-bot detection. Malicious web spots are known to pose a significant threat to the entire Internet community. One particularly challenging form of malicious bot are the bots capable of impersonating human behavior in terms of mouse movement. The latest generation of such human-mimicking malicious bots are synthesized by means of'session replays' [6, 7, 8]. That is, these bots programmatically replay a browsing session, including the mouse movement trajectory, that was previously executed (and recorded) by a genuine human visitor to a target/victim web site. The specific goal of this study is to offer a better insight into: (a) the statistical similarities and differences between browsing sessions (mouse movement trajectories) generated by different genuine users on the same target web page; (b) the statistical similarities and differences between browsing sessions (mouse movement trajectories) repeated by the same genuine user on the same target web page. We believe that a better understanding of these similarities and differences is of critical importance for the creation of more effective techniques of malicious bot detection--in particular the detection of session-replay bots--which in turn can ensure a safer Internet for everyone. The specific contributions of the research work presented in this paper can be summarized as follows: (i) We developed an interactive web platform capable of collecting a number of different mouse movement actions and features, including trajectory, point-click, drag-and-drop, velocity, etc.