A Survey of Keystroke Dynamics Biometrics <s> Development Platform. <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> Development Platform. <s> This paper describes a simple, software based keyboard monitoring system for the IBM PC for the continuous analysis of the typing characteristics of the user for the purpose of continuous authentication. By exploiting the electrical characteristics of the PC keyboard interface together with modifications to the internal system timer, very accurate measurements can be made of keystroke interval and duration, including measurements of rollover. Rollover patterns, particularly when typing common diphthongs, can be highly characteristic of individual users and provide quite an accurate indication of the users identity. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Development Platform. <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> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Development Platform. <s> Smart phones are now being used to store users' identities and sensitive information/data. Therefore, it is important to authenticate legitimate users of a smart phone and to block imposters. In this paper, we demonstrate that keystroke dynamics of a smart phone user can be translated into a viable features' set for accurate user identification. To this end, we collect and analyze keystroke data of 25 diverse smart phone users. Based on this analysis, we select six distinguishing keystroke features that can be used for user identification. We show that these keystroke features for different users are diffused and therefore a fuzzy classifier is well-suited to cluster and classify them. We then optimize the front-end fuzzy classifier using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as back-end dynamic optimizers to adapt to variations in usage patterns. Finally, we provide a novel keystroke dynamics based PIN (Personal Identification Number) verification mode to ensure information security on smart phones. The results of our experiments show that the proposed user identification system has an average error rate of 2% after the detection mode and the error rate of rejecting legitimate users drops to zero in the PIN verification mode. We also compare error rates (in terms of detecting both legitimate users and imposters) of our proposed classifier with 5 existing state-of-the-art techniques for user identification on desktop computers. Our results show that the proposed technique consistently and considerably outperforms existing schemes. <s> BIB004 </s> A Survey of Keystroke Dynamics Biometrics <s> Development Platform. <s> Legitimate user authentication is an important part of the problems related to computer and system security. The maintenance of security becomes even more difficult when an invalid user gets the system access information. The paper presents a suite of techniques for password authentication using neural networks, fuzzy logic, statistical methods, and several hybrid combinations of these approaches. The approaches presented in the paper use typing biometrics of a user, in addition to conventional login information, to identify a user. <s> BIB005 </s> A Survey of Keystroke Dynamics Biometrics <s> Development Platform. <s> Unlike other access control systems based on biometric features, keystroke analysis has not led to techniques providing an acceptable level of accuracy. The reason is probably the intrinsic variability of typing dynamics, versus other---very stable---biometric characteristics, such as face or fingerprint patterns. In this paper we present an original measure for keystroke dynamics that limits the instability of this biometric feature. We have tested our approach on 154 individuals, achieving a False Alarm Rate of about 4p and an Impostor Pass Rate of less than 0.01p. This performance is reached using the same sampling text for all the individuals, allowing typing errors, without any specific tailoring of the authentication system with respect to the available set of typing samples and users, and collecting the samples over a 28.8-Kbaud remote modem connection. <s> BIB006 </s> A Survey of Keystroke Dynamics Biometrics <s> Development Platform. <s> As mobile devices continue to evolve in terms of the capabilities and services offered, so they introduce additional demands in terms of security. An issue that has traditionally been poorly served is user authentication, with the majority of devices relying upon problematic secret knowledge approaches. This paper proposes the use of more advanced biometric methods as an alternative. After considering the general range of available techniques and their applicability to mobile devices, the discussion focuses upon the concept of keystroke analysis. Results of a practical evaluation are presented based upon the entry of both telephone numbers and text messages on a mobile phone. The findings reveal the technique to have promise for certain users with average error rates below 5%. The paper then proceeds to explain how the accuracy could be further improved by incorporating keystroke analysis within a composite authentication mechanism that utilises a portfolio of authentication techniques to provide robust, accurate and transparent authentication of the user. <s> BIB007 </s> A Survey of Keystroke Dynamics Biometrics <s> Development Platform. <s> abstract Keystroke dynamics are becoming a well-known method for strengthening username- and password-based credential sets. The familiarity and ease of use of these traditional authentication schemes combined with the increased trustworthiness associated with biometrics makes them prime candidates for application in many web-based scenarios. Our keystroke dynamics system uses Breiman’s random forests algorithm to classify keystroke input sequences as genuine or imposter. The system is capable of operating at various points on a traditional ROC curve depending on application-specific security needs. As a username/password authentication scheme, our approach decreases the system penetration rate associated with compromised passwords up to 99.15%. Beyond presenting results demonstrating the credential hardening effect of our scheme, we look into the notion that a user’s familiarity to components of a credential set can non-trivially impact error rates. <s> BIB008 </s> A Survey of Keystroke Dynamics Biometrics <s> Development Platform. <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> BIB009 </s> A Survey of Keystroke Dynamics Biometrics <s> Development Platform. <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> BIB010 </s> A Survey of Keystroke Dynamics Biometrics <s> Development Platform. <s> We have investigated several characteristics of keystroke dynamics in Japanese long-text input. We performed experiments with 189 participants, classified into three groups according to the number of letters they could type in five minutes. In this experimental study, we extracted feature indices from the keystroke timing for each alphabet letter and for each two-letter combination composed of a consonant and vowel in Japanese text. Taking into account two identification methods using Weighted Euclidean Distance (WED) and Array Disorder (AD), we proposed WED+AD method for authentication on the basis of keystroke data in Japanese long-text input. By evaluating the authentication performance of individuals in the three groups, the effectiveness of the method was found to correspond to the typing skill level of the group. <s> BIB011 </s> A Survey of Keystroke Dynamics Biometrics <s> Development Platform. <s> Smartphones are increasingly being used to store personal information as well as to access sensitive data from the Internet and the cloud. Establishment of the identity of a user requesting information from smartphones is a prerequisite for secure systems in such scenarios. In the past, keystroke-based user identification has been successfully deployed on production-level mobile devices to mitigate the risks associated with naive username/password based authentication. However, these approaches have two major limitations: they are not applicable to services where authentication occurs outside the domain of the mobile device--such as web-based services; and they often overly tax the limited computational capabilities of mobile devices. In this paper, we propose a protocol for keystroke dynamics analysis which allows web-based applications to make use of remote attestation and delegated keystroke analysis. The end result is an efficient keystroke-based user identification mechanism that strengthens traditional password protected services while mitigating the risks of user profiling by collaborating malicious web services. We present a prototype implementation of our protocol using the popular Android operating system for smartphones. <s> BIB012
Since the most common user interaction involving text and numerical input is through a personal computer, researchers who were working on keystroke dynamics are almost all based on local computer platform. Before the 21st century, keystroke dynamics experiment prototype was developed on operating system (OS) platform using third-generation programming language (3GL) such as FORTRAN BIB001 and Turbo Pascal BIB002 . Later when Microsoft products dominate most operating system, an experimental prototype was built on top of MS DOS BIB003 and windows environment BIB006 by using languages such as C++ BIB005 and Visual Basic  . Owing to the pace of internet development in the last decade, experimental platform has been shifted to the webbased environment BIB010 with web programming tools such as JavaScript BIB011 , Java Applet BIB008 , and Flash BIB009 . It is only in the last couple of years; several works have been developed based on mobile device environment. Starting off with mobile emulator BIB007 , Symbian operating system BIB004 , and most recently Android platform BIB012 . The association of development platform with keystroke dynamics research works in the literature can be summarized as OS (44%), web (17%), mobile (5%), and unknown (34%).