## 2 Background and Related Work
They tested their approach using 15 participants of age 22-30. They achieved an error rate of 20% when using 1.5 standard deviations of the average from the corresponding enrolment value, and an error rate of 15 percent using 1 standard deviation of the average from the corresponding enrolment value. During the enrollment phase for passive authentication, they ran the program in the background to record the mouse coordinates for a shorter period of time; it was only 15 min for each participant. [PERSON] and [PERSON] (2004) [19] developed a data acquisition system for collecting users' mouse activities. The system records all user interaction throughout the world wide web. The dataset was collected from 50 participants; each user had 400 strokes. A stroke is defined as a group of points between two actions. The authors proposed 58 behavioral features extracted from the raw data using some mathematical operations. These features were used to identify a user on the basis of how they interact with the system. Furthermore, the authors developed a sequential classifier using statistical pattern recognition techniques in order to distinguish between users. The authors achieved an equal error rate of 0.7% per 100 mouse strokes. The system used only a user's interaction characteristics, not the user's performance. [PERSON] and [PERSON] (2004) [20] proposed a re-authentication approach using a user's mouse activities. They collected raw data from 11 student volunteers who spent about two hours on their own personal computers under an uncontrolled environment. The volunteers used Internet Explorer on a Windows computer for data collection. The experiment focused on using only Internet Explorer applications in order to decrease the difficulty of discriminating among the users' behaviors. The researchers used a supervised learning method, C5.0 decision trees. However, they split the dataset into 70% for training and 30% for testing only. They obtained an average false acceptance rate (FAR) of 1.75% and average false rejection rate (FRR) of 0.43%. The authors did not collect the client area mouse movements because their rate of occurrence was high. [PERSON] and [PERSON] (2007) [1] divided the types of mouse actions into three categories: mouse movement (MM), point-click (PC), and drag-and-drop (DD). More recently, [PERSON] and [PERSON] ([PERSON] and [PERSON], 2007, 2011) published a subsequent study based on the same dataset and using all three types of mouse actions. They collected their data from 22 participants over 998 sessions and conducted experiments on user authentication. [PERSON] and [PERSON] (2007) proposed a new form of behavioral biometrics via computer mouse dynamics: a detection technique using a neural network. They achieved relatively high false acceptance rate (FAR) of 2.4649% and a false rejection rate (FRR) of 2.4614%. [PERSON] and [PERSON] (2011) [21] described the measurement results on an extended dataset of 48 users. They proposed a mouse dynamics biometric recognition system for commercial user identification. A fuzzy classifier technique was used to merge corresponding biometric scores. They reported results of a false acceptance rate of 0% and a false rejection rate of 0.36%. In contrast, some studies used only two types of mouse actions. [PERSON] et al. (2011) designed a reliable and effective continuous authentication mechanism by using only point-click (PC) and mouse movement (MM) actions, as defined by [PERSON] and [PERSON] ([PERSON] and [PERSON], 2007). They used the support-vector machine (SVM) classifier for user verification. The results showed that their novel system to verify a user achieved an equal error rate (EER) of 1.3% using mouse clicks and 1.9% using mouse movements. Such performance does not meet the European standard for access control, which requires a commercial biometric system to achieve a FAR of less than 0.001% and a FRR of less than 1%.