A Survey of Keystroke Dynamics Biometrics <s> Static Authentication Mode. <s> Password is the most widely used identity verification method in computer security domain. However, due to its simplicity, it is vulnerable to imposter attacks. Keystroke dynamics adds a shield to password. Discriminating imposters from owners is a novelty detection problem. Recent research reported good performance of Auto-Associative Multilayer Perceptron(AaMLP). However, the 2-layer AaMLP cannot identify nonlinear boundaries, which can result in serious problems in computer security. In this paper, we applied 4-layer AaMLP as well as SVM as novelty detector to keystroke dynamics identity verification, and found that they can significantly improve the performance. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Static Authentication Mode. <s> Keystroke dynamics is a behavioral biometric that is based on how a user enters their login details. In this study, a set of eight attributes were extracted during the course of entering login details. This collection of attributes was used to form a reference signature (a biometrics identification record) for subsequent authentication requests. The algorithm for the authentication step entails transforming the attributes into a discretised form based on the amino acid alphabet. A set of bioinformatics based algorithms are then used to perform the actual authentication test. In addition, the use of rough sets was employed in this study to determine if subsets of attributes were more important in the classification (authentication) than others. Lastly, the results of this study indicate that the error rate is less than 1% in the majority of the cases. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Static Authentication Mode. <s> In this paper, we present a novel keystroke dynamic recognition system by means of a novel two-layer fusion approach. First, we extract four types of keystroke latency as the feature from our dataset. The keystroke latency will be transformed into similarity scores via Gaussian Probability Density Function (GPD). We also propose a new technique, known as Direction Similarity Measure (DSM), which measures the absolute difference between two sets of latency. Last, four fusion approaches coupled with six fusion rules are applied to improve the final result by combining the scores that are produced by GPD and DSM. Best result with equal error rate of 1.401% is obtained with our two-layer fusion approach. <s> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Static Authentication Mode. <s> When typing on a keyboard a user can be authenticated through what he/she types (username, password), but also through how he/she types, that is, through keystroke dynamics. We study whether authentication through keystroke dynamics is sufficiently reliable as a security instrument to be used together with the more standard instruments. Based on a data set of 1254 participants who typed the same username and password, 20 times each, we develop a test statistic and obtain the power of our test. We conclude that keystroke dynamics can be a reliable security instrument for authentication, if used together with other instruments. It seems more suitable for authentication (verification) than for identification. Dwell times (how long a key is held pressed) are more discriminatory and therefore more powerful than flight times (time between consecutive press times). <s> BIB004 </s> A Survey of Keystroke Dynamics Biometrics <s> Static Authentication Mode. <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> BIB005
Both dwell time (DT) and flight time (FT) are often extracted as feature vector for static authentication. There was no clear comparison made on which timing vector performed the best; however, BIB003 suggested that the combination of both DT and FT produced a better result than using them independently. The best combination of keystroke features and methods yield a respectable EER of 1.401%. By far the experiment that involved the largest number of participants was conducted by BIB004 . A whooping 1254 users were involved, although only half of that amount completed the whole data collection process. Experiment with around 100 users is considered moderate in keystroke dynamics domain thus far as seen in Tables 4 and 5 . By using an autoassociative multilayer perceptron algorithm and support vector machine as novelty detector, BIB001 was able to attain impressive result of nearly 0% for both FAR and FRR. In spite of good performance, users were required to repeatedly provide their password 150 to 400 times, which may not be feasible in real world situation. Furthermore keystroke samples at the later repetition may be significantly different from the initial few ones as user gets accustom to the input text. Therefore, the best practice would be to perform data collection over a few sittings. At such, user will not be burdened by large repetitive inputs and the keystroke feature captured reflects the gradual change in typing pattern due to familiarization over time. For example in BIB005 , data collection was scattered across five sessions separated by one week apart with 12 repetitions of input samples per session. Another interesting experimental variable is the degree of freedom user is given during data collection phase. Numerous research works confined user to a predefined input text and yet yielded reasonable performance (EER < 5%) such as BIB003  BIB002 . These results may be improved further, in particular FRR, if users are allowed to choose their own favourable string. The argument here is that familiarity of a certain string will most likely promote consistency, thereby reducing intraclass variability. Therefore, if an experiment consists of both fixed string and user selected string, comparison between the effects of input string selection can be deduced. Similarly, the effect of user typing on a familiar verses prearranged device may cast some significance to the recognition performance. Although it may not be entirely possible to provide such flexibility due to various reasons and constrains, it is seen as a potential consideration in terms of experimental design for future research work.