## 1 Introduction  
In the current age of internet technology, authentication is a major security issue because authentication failures often cause detrimental effects. Recognition of masquerading is a crucial factor in preventing identity theft. Username/password-based applications routinely used in social networks, e-mail, e-commerce, and accessing online banking are not sufficiently protective technologies due to the increasing number of hacking attacks [1]. Theft of passwords can result in massive damage to individuals and companies. Computer security is becoming increasingly important for both individual users and society with the increase in communication between the services and devices in use. On online security systems, continuous authentication systems (CAs), and anomaly detection systems (ADs) are necessary to handle the rise in unauthorized access. Biometrics make the authentication stronger and uniquely determine a user's identity. The working principle of a biometric authentication system is to use a person's physical and behavioral traits. The physical biometrics used to detect fraudulent identification are the iris, the face, the fingerprints, and the voice. Their use is an intrusive method that provides sufficient authentication [1, 2]. Physical biometrics are often considered accurate for authentication, but their use faces potential privacy concerns [3]. Behavioral biometrics on the other hand is an alternative approach to physical biometrics that provides an additional protection system against fraudulent identification [4, 5]. The principle of behavioral biometrics is based on behavioral traits such as a person's handwriting patterns, gait, mouse dynamics, and keystrokes [6, 7]. Behavioral biometrics is a quantifiable method that generates behavioral profiles of users. Many behavioral biometrics studies have been done using CA and AD to detect intruders. Behavioral biometrics provide several advantages over traditional methods of authentication. For instance, a person's mouse behavior is not intrusive but is capable of providing continuous authentication. Furthermore, behavioral biometrics analysis of a person's keyboard usage behavior does not require access to the person's sensitive data. These advantages have led to an increase on research on the use of mouse dynamics biometrics in user-authentication systems [3; 8; 9]. Mouse dynamics is one of the behavioral biometrics that can save and analyze actions from a mouse input device such as general movement, drag-and-drop, and point-click actions while a person interacts with a specific graphical user interface [5; 10]. Both online CA and AD techniques are other promising techniques that are capable of addressing the vulnerability in a static one-time authentication. These techniques cannot be used for a primary login, but they can be used to provide additional security, detect malicious actions, and mitigate the expected risk associated with the attacks and abnormal activities of online networks [11]. There are a lot of previous research experiments that have drawn conclusions on the basis of data only collected from a client's desktop, while there are a significantly lower number of experiments that focus on the online domain [12]. This study is restricted to the online domain for data collection by using an online game instead of other general GUI environments. A software program was developed to capture users' interaction via a pointing device based on two phases: a continuous authentication phase, and an anomaly detection phase. An empirical biometric-based study of different machine learning (ML) and deep learning (DL) techniques including decision tree classifier (DT), \(k\)-nearest neighbor classifier (KNN), random forest classifier (RF), and convolutional neural network classifier (CNN) was conducted in this research. The following are the contributions that this study provides:  
* An overview of existing techniques related to CA and AD is given, as well as the methods in which these have been used.