A Survey of Keystroke Dynamics Biometrics <s> Retraining Module. <s> The purpose of novelty detection is to detect (novel) patterns that are not generated by the identical distribution of the normal class. A distance-based novelty detector classifies a new data pattern as ''novel'' if its distance from ''normal'' patterns is large. It is intuitive, easy to implement, and fits naturally with incremental learning. Its performance is limited, however, because it relies only on distance. In this paper, we propose considering topological relations as well. We compare our proposed method with 13 other novelty detectors based on 21 benchmark data sets from two sources. We then apply our method to a real-world application in which incremental learning is necessary: keystroke dynamics-based user authentication. The experimental results are promising. Not only does our method improve the performance of distance-based novelty detectors, but it also outperforms the other non-distance-based algorithms. Our method also allows efficient model updates. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Retraining Module. <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> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Retraining Module. <s> Among all the existing biometric modalities, authentication systems based on keystroke dynamics present interesting advantages. These solutions are well accepted by users and cheap as no additional sensor is required for authenticating the user before accessing to an application. In the last thirty years, many researchers have proposed, different algorithms aimed at increasing the performance of this approach. Their main drawback lies on the large number of data required for the enrollment step. As a consequence, the verification system is barely usable, because the enrollment is too restrictive. In this work, we propose a new method based on the Support Vector Machine (SVM) learning satisfying industrial conditions (i.e., few samples per user are needed during the enrollment phase to create its template). In this method, users are authenticated through the keystroke dynamics of a shared secret (chosen by the system administrator). We use the GREYC keystroke database that is composed of a large number of users (100) for validation purposes. We compared the proposed method with six methods from the literature (selected based on their ability to work with few enrollment samples). Experimental results show that, even though the computation time to build the template can be longer with our method (54 s against 3 s for most of the others), its performance outperforms the other methods in an industrial context (Equal Error Rate of 15.28% against 16.79% and 17.02% for the two best methods of the state-of-the-art, on our dataset and five samples to create the template, with a better computation time than the second best method). <s> BIB003
Keystroke dynamics biometrics are behavioural traits, which implie that it is impossible to acquire an exact typing pattern of even from the same individual. This is useful for authentication, whereby the distinctiveness can be used to differentiate one's keystroke dynamics from anothers. On the other hand, it may also cause problems due to intraclass variability. A solution is needed to compensate the changes of legitimate user's gradual typing cadence over time. Retraining refers to the recapture and refinement of users' biometric template upon successful verification of their biometric credential  . It is also known as incremental learning procedure BIB001 , template update mechanism BIB002 , and adaptive module BIB003 . If keystroke template remains unaccustomed to the gradually shift of typing pattern over time, system accuracy will be degraded over time. According to BIB002 , 50% of improvement can be gained by having this module in place. However, the number of research works that engaged with retraining module is limited to only less than 20% among 187 literatures studied.