A Survey of Keystroke Dynamics Biometrics <s> Data Type. <s> An approach to securing access to computer systems is described. By performing real-time measurements of the time durations between the keystrokes when a password is entered and using pattern-recognition algorithms, three online recognition systems were devised and tested. Two types of passwords were considered: phrases and individual names. A fixed phrase was used in the identification system. Individual names were used as a password in the verification system and in the overall recognition system. All three systems were tested and evaluated. The identification system used 10 volunteers and gave an indecision error of 1.2%. The verification system used 26 volunteers and gave an error of 8.1% in rejecting valid users and an error of 2.8% in accepting invalid users. The overall recognition system used 32 volunteers and gave an error of 3.1% in rejecting valid users and an error of 0.5% in accepting invalid users. > <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Data Type. <s> This paper presents techniques to verify the identity of computer users using the keystroke dynamics of computer user's login string as characteristic patterns using pattern recognition and neural network techniques. This work is a continuation of our previous work where only interkey times were used as features for identifying computer users. In this work we used the key hold times for classification and then compared the performance with the former interkey time-based technique. Then we use the combined interkey and hold times for the identification process. We applied several neural network and pattern recognition algorithms for verifying computer users as they type their password phrases. It was found that hold times are more effective than interkey times and the best identification performance was achieved by using both time measurements. An identification accuracy of 100% was achieved when the combined hold and intekey time-based approach were considered as features using the fuzzy ARTMAP, radial basis function networks (RBFN), and learning vector quantization (LVQ) neural network paradigms. Other neural network and classical pattern algorithms such as backpropagation with a sigmoid transfer function (BP, Sigm), hybrid sum-of-products (HSOP), sum-of-products (SOP), potential function and Bayes' rule algorithms gave moderate performance. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Data Type. <s> This paper presents a novel application of neural nets to user identity authentication on computer-access security system. Keystroke latency is measured for each user and forms the patterns of keyboard dynamics. A three-layered backpropagation neural network with a flexible number of input nodes was used to discriminate valid users and impostors according to each individual's password keystroke pattern. System verification performance was improved by setting convergence criteria RMSE to a smaller threshold value during training procedure. The resulting system gave an 1.1% FAR (false alarm rate) in rejecting valid users and zero IPR (impostor pass rate) in accepting no impostors. The performance of the proposed identification method is superior to that of previous studies. A suitable network structure for this application was also discussed. Furthermore, the implementation of this approach requires no special hardware and is easy to be integrated with most computer systems. <s> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Data Type. <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> BIB004 </s> A Survey of Keystroke Dynamics Biometrics <s> Data Type. <s> This paper presents a new approach for biometric authentication based on keystroke dynamics through numerical keyboards. The input signal is generated in real time when the user enters with target string. Five features were extracted from this input signal (ASCII key code and four keystroke latencies) and four experiments using samples for genuine and impostor users were performed using two pattern classification technics. The best results were achieved by the HMM (EER=3.6%). This new approach brings security improvements to the process of user authentication, as well as it allows to include biometric authentication in mobile devices, such as cell phones. <s> BIB005 </s> A Survey of Keystroke Dynamics Biometrics <s> Data Type. <s> Keystroke time intervals can be a discriminating feature in the verification and identification of computer users. This paper presents a comparison result obtained using several classification methods including ?-NN (?-Nearest Neighbor), back-propagation neural networks, and Bayesian classification for keystroke identification. Performance of ?-NN classification was best with small data samples available per user, while Bayesian classification was the most superior to others with large data samples per user. Thus, for web-based on-line identification of users, it seems to be appropriate to selectively use either ?-NN or Bayesian method according to the number of keystroke samples accumulated by each user. <s> BIB006 </s> A Survey of Keystroke Dynamics Biometrics <s> Data Type. <s> Biometrics technology has emerged as the desired solution for a range of secure applications where a person's identity must be determined. Biometrics is currently controlling network and physical access in numerous high risk areas, including airport customs and immigration desks, financial malls among others. This paper compares applications of neural networks to the field of hardened password mechanism in a typical workplace environment. <s> BIB007 </s> A Survey of Keystroke Dynamics Biometrics <s> Data Type. <s> Keystroke typing characteristics is considered as one of the important biometric features that can be used to protect users against malicious attacks. In this paper we propose a statistical model for web authentication with keystroke typing characteristics based on Hidden Markov Model and Gaussian Modeling from Statistical Learning Theory. Our proposed model can substantially enhance the accuracy of the identity authentication by analyzing keystroke timing information of the username and password. Results of the experiments showed that our scheme achieved by far the best error rate of 2.54%. <s> BIB008 </s> A Survey of Keystroke Dynamics Biometrics <s> Data Type. <s> The present invention provides apparatus for changing the direction of rotation of a rotating shaft without changing the direction of rotation of the motor driving the shaft. The present invention includes a drive gear which causes two driven bevel gears to rotate in opposite directions. A control shaft having cam surfaces selectively forces one of two sets of balls into engagement with one of the driven gears to couple that gear with the driven shaft to rotate the shaft in one direction. Rotation of the driven shaft is reversed by changing the position of the control shaft which uncouples one set of balls from the driven shaft and couples the remaining set of balls to the driven shaft. <s> BIB009 </s> A Survey of Keystroke Dynamics Biometrics <s> Data Type. <s> We developed Web-based typing software in order to collect keystroke data on a LAN, and investigated several characteristics of keystroke dynamics in Japanese free text typing. We performed experiments on 112 subjects, representing three groups according to the number of letters they could type in five minutes. In this experiment, we extracted the feature indices from the keystroke timing for each single letter and for two-letter combinations composed of consonant and vowel pairs in Japanese text. Based on an identification method using a weighted Euclid distance, personal identification for the three groups was evaluated and its high performance was confirmed in proportion to the typing level of the group. <s> BIB010 </s> A Survey of Keystroke Dynamics Biometrics <s> Data Type. <s> Keystroke dynamics biometric systems have been studied for more than twenty years. They are very well perceived by users, they may be one of the cheapest biometric system (as no specific material is required) even if they are not commonly spread and used [1]. We propose in this paper a new method based on SVM learning satisfying operational conditions (no more than 5 captures for the enrollment step). In the proposed method, users are authenticated thanks to keystroke dynamics of a passphrase (that can be chosen by the system administrator). We use the GREYC keystroke benchmark that is composed of a large number of users (100) for validation purposes. We tested the proposed method face to four other methods from the state of the art. Experimental results show that the proposed method outperforms them in an operational context. <s> BIB011 </s> A Survey of Keystroke Dynamics Biometrics <s> Data Type. <s> Traditionally users are authenticated based on a username and password. However, a logged station is still vulnerable to imposters when the user leaves her computer without logging off. Keystroke dynamics methods can be useful to continuously verify a user, after the authentication process has successfully ended. Within the last decade several studies proposed the use of keystroke dynamics as a behavioral biometric tool to verify users. We propose a new method, for compactly representing the keystroke patterns by joining similar pairs of consecutive keystrokes. The proposed method considers clustering di-graphs based on their temporal features. The proposed method was evaluated on 10 legitimate users and 15 imposters. Encouraging results suggest that the proposed method detection performance is better than that of existing methods. Specifically we reach a False Acceptance Rate (FAR) of 0.41% and a False Rejection Rate (FRR) of 0.63%. <s> BIB012 </s> A Survey of Keystroke Dynamics Biometrics <s> Data Type. <s> Internet services are important part of daily activities for most of us. These services come with sophisticated authentication requirements which may not be handled by average Internet users. The management of secure passwords for example creates an extra overhead which is often neglected due to usability reasons. Furthermore, password-based approaches are applicable only for initial logins and do not protect against unlocked workstation attacks. In this paper, we provide a non-intrusive identity verification scheme based on behavior biometrics where keystroke dynamics based-on free-text is used continuously for verifying the identity of a user in real-time. We improved existing keystroke dynamics based verification schemes in four aspects. First, we improve the scalability where we use a constant number of users instead of whole user space to verify the identity of target user. Second, we provide an adaptive user model which enables our solution to take the change of user behavior into consideration in verification decision. Next, we identify a new distance measure which enables us to verify identity of a user with shorter text. Fourth, we decrease the number of false results. Our solution is evaluated on a data set which we have collected from users while they were interacting with their mail-boxes during their daily activities. <s> BIB013
In general, experimental subjects are required to either provide character-based text or purely numerical inputs BIB005 . The majority of research works with character-based inputs are illustrated in Figure 4 . The input type can be further subdivided into long or short text. Short inputs normally consist of username BIB002  , password BIB004 BIB003 , or text phrase BIB001 BIB007 , while long inputs are usually referred to paragraphs of text enclosing 100 words or more BIB009 BIB010 . Freedom of input is another determinant factor that distinguishes keystroke dynamics research. The evaluation that requests experimental subject to type a predetermined input BIB011 BIB006 has the advantage of utilizing sample data from different users in the same database pool. This method significantly increases the number of imposter samples without the need of collecting them separately. On the other hand, an experiment that offers the flexibility of input data may require more efforts to collect additional test data BIB003 BIB008 . Having said that, user defined input resembles closer to real world scenario than fixed text. Furthermore, it is infeasible to constrain the input text in some cases such as BIB013 BIB012  , due to the nature and objective of the experiment where the user 8 The Scientific World Journal must have the freedom of input. Therefore, the number of research works on both types of inputs is fairly even.