A Survey of Keystroke Dynamics Biometrics <s> Input Repetition. <s> User authentication is an important problem for any restricted access computer system, especially with remote access. Constant spread of Internet makes efficient remote authentication a pressing issue while the distributed nature of interaction introduces additional difficulties. Authentication methods based on most biometrics techniques require dedicated hardware that is unhandy for remote applications. In contrast keystroke dynamics requires only limited dedicated software and does not require any dedicated hardware. The approach we propose is aimed at efficient user authentication with keystroke dynamics using short fixed text, that may for example occur simultaneously to logging process. We have tested the suggested approach on a large group of individuals, with data gathered over Internet using browser-based WWW application. The obtained results are promising and encourage further development in this area. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Input Repetition. <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> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Input Repetition. <s> Keystroke Dynamics, which is a biometric characteristic that depends on typing style of users, could be a viable alternative or a complementary technique for user authentication if tolerable error rates are achieved. Most of the earlier studies on Keystroke Dynamics were conducted with irreproducible evaluation conditions therefore comparing their experimental results are difficult, if not impossible. One of the few exceptions is the work done by Killourhy and Maxion, which made a dataset publicly available, developed a repeatable evaluation procedure and evaluated the performance of different methods using the same methodology. In their study, the error rate of neural networks was found to be one of the worst-performing. In this study, we have a second look at the performance of neural networks using the evaluation procedure and dataset same as in Killourhy and Maxion's work. We find that performance of artificial neural networks can outperform all other methods by using negative examples. We conduct comparative tests of different algorithms for training neural networks and achieve an equal error rate of 7.73% with Levenberg-Marquardt backpropagation network, which is better than equal error rate of the best-performing method in Killourhy and Maxion's work. <s> BIB003
In order to generate reference template, several instances of sample data are required. The greater amount of samples used in constructing reference template, the closer it resembles one's typing model BIB002 and recognition rate may also be potentially better as proven by BIB001 BIB003 . However, it is infeasible to collect large number of sample data during enrolment stage. Therefore, a balance should be struck while selecting the optimal number of sample repetition for an experiment. According to the trend in the literature, the benchmark was positioned at less than ten as shown in Figure 5 . Nevertheless, sample collection can be divided into several sessions over a period of time, thereby not only reducing the initial load but also reflecting typing variability (further discussion will be given in the following section).