A Survey of Keystroke Dynamics Biometrics <s> Continuous Monitoring and Authentication. <s> Keystroke dynamics based authentication (KDA) verifies a user based on the typing pattern. During enroll, a few typing patterns are provided, which are then used to train a classifier. The typing style of a user is not expected to change. However, sometimes it does change, resulting in a high false reject. In order to achieve a better authentication performance, we propose to continually retrain classifiers with recent login typing patterns by updating the training data set. There are two ways to update it. The moving window uses a fixed number of most recent patterns while the growing window uses all the new patterns as well as the original enroll patterns. We applied the proposed method to the real data set involving 21 users. The experimental results show that both the moving window and the growing window approach outperform the fixed window approach, which does not retrain a classifier. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Continuous Monitoring and Authentication. <s> Online examinations pose a unique problem for distance-based education, in that it can be very difficult to provide true user authentication. Due to the inherent anonymity of being online, compared to taking an examination in a classroom environment, students may attempt to artificially boost their scores in online examinations by having another individual take the exam for them, which a typical user/password authentication scheme cannot detect. This paper discusses and presents a method for providing continuous biometric user authentication in online examinations via keystroke dynamics. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Continuous Monitoring and Authentication. <s> This paper describes a novel technique to strengthen password authentication system by incorporating multiple keystroke dynamic information under a fusion framework. We capitalize four types of latency as keystroke feature and two methods to calculate the similarity scores between the two given latency. A two layer fusion approach is proposed to enhance the overall performance of the system to achieve near 1.401% Equal Error Rate (EER). We also introduce two additional modules to increase the flexibility of the proposed system. These modules aim to accommodate exceptional cases, for instance, when a legitimate user is unable to provide his or her normal typing pattern due to reasons such as hand injury. <s> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Continuous Monitoring and Authentication. <s> Keystroke dynamics is a behavioral biometrics showing a degradation of performance when used over time. This is due to the fact that the user improves his/her way of typing while using the system, therefore the test samples may be different from the initial template computed at an earlier stage. One way to bypass this problem is to use template update mechanisms. We propose in this work, new semi-supervised update mechanisms, inspired from known supervised ones. These schemes rely on the choice of two thresholds (an acceptance threshold and an update threshold) which are fixed manually depending on the performance of the system and the level of tolerance in possible inclusion of impostor data in the update template. We also propose a new evaluation scheme for update mechanisms, taking into account performance evolution over several time-sessions. Our results show an improvement of 50% in the supervised scheme and of 45% in the semi-supervised one with a configuration of the parameters chosen so that we do not accept many erroneous data. <s> BIB004
Continuous monitoring and authentication have often been sidelined yet they are relatively important. Keystroke dynamics biometrics offer a way to continuously validate BIB002 the legitimate identity of a user. As long as user interaction with the system through input devices persists, keystroke pattern can be constantly monitored and reevaluated. BIB001 BIB003 BIB004 that may resolve this issue.