A Survey of Keystroke Dynamics Biometrics <s> Keystroke Dynamic Acquisition Device <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> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Keystroke Dynamic Acquisition Device <s> Keystroke dynamics refers to a userpsilas habitual typing characteristics. These typing characteristics are believed to be unique among large populations. In this paper, we present a novel keystroke dynamic recognition system by using a fusion method. Firstly,we record the dwell time and the flight time as the feature data. We then calculate their mean and standard deviation values and stored. The test feature data will be transformed into the scores via Gaussian probability density function. On the other hand, we also propose a new technique, known as Direction Similarity Measure (DSM) to measure the differential of sign among each coupled characters in a phrase. Lastly, a weighted sum rule is applied by fusing the Gaussian scores and the DSM to enhance the final result. The best result of equal error rate 6.36% is obtained by using our home-made dataset. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Keystroke Dynamic Acquisition Device <s> In pattern recognition, Bayes classifier is considered a powerful tool in the decision making process as its gives the lowest probability of committing a classification error. This has been highlighted in a number of previous works. The diversity in algorithm parameters, criteria used and number of users involved as well as the evaluation method makes the task of comparing their results and selecting an appropriate system a very thorny one. The purpose of this paper is four fold: to study the different approaches reported using a true password, to establish how normalization and using a single variance/mean impact on the results, using a maximum number of features does necessarily improve performance and finally demonstrate that performance optimization is feasible through careful selection of features and approach taken. <s> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Keystroke Dynamic Acquisition Device <s> Now mobile devices are developed to serve various functions, storing the sensitive information. In order to protect those information and mobile systems from unauthorized users, the authentication system must be installed unavoidably. Additionally, the development of the mobile system is moving forward to the touch screen system for user friendly and quick access mechanism. In this paper, we proposed behavioral manners of users over the touchpad acting like touch screen that is able to detect the finger pressure. These behaviors are keystroke dynamics and the finger pressure. The finding has shown that, the finger pressure gives the discriminative information more than keystroke dynamics with the k-NN analytical method. Moreover, using only the finger pressure produces high accuracy rate of 99%. <s> BIB004 </s> A Survey of Keystroke Dynamics Biometrics <s> Keystroke Dynamic Acquisition Device <s> Analysis of keystroke dynamics can be useful in protecting personal data because an individual is authenticated not only by password, but also by that individual's keystroke patterns. In this way, intrusion becomes more difficult because the username/password pair, as well as the typing speed and correct keystroke pattern must both be duplicated. The purpose of this paper is to present a keystroke analysis tool that can be incorporated into distributed systems and web-based services. This study also assesses the potential of keystroke analysis as a complementary authentication mechanism. Eleven individuals entered a password into specially developed keystroke analysis software twenty times over a course of four sessions. The data were statistically analyzed to determine keystroke patterns. Tests were performed to verify whether the users could be properly authenticated. Results show that authentication with mean key press timings resulted in very good false acceptance rates, while allowing access to appropriate users. <s> BIB005 </s> A Survey of Keystroke Dynamics Biometrics <s> Keystroke Dynamic Acquisition Device <s> The objective of the paper is to provide how a biopassword is using keystroke dynamics technology to deliver security for mobile devices to monitor and authenticate users. Mobile devices have outgrown their initial use for telephony and now have added functionality such web browsing and m-commerce. Depending on the specific area of usage of the device, the importance and hence the sensitivity of the data stored in it changes. As a result, there is a need to secure the data and to protect the devices from loss or theft. The users of these mobile devices have authentication method of PIN i.e. (Personal Identification Numbers). But, with the problems associated with using PIN is Easy to Crack password's, Sharing the passwords, using the same password for different devices, the need for using sophisticated methods allowing only the legitimate users the access to data and identifying and preventing imposters manifolds many times as the mobility of the computing devices changes. In this paper we examine an emerging biometric technique that aims to identify users based on analyzing habitual rhythm patterns in the way they type. <s> BIB006
3.1.1. Normal Hardware. One of the prime benefits of keystroke dynamics biometrics is low dependency on dedicated hardware infrastructure. For that reason, it is selfexplanatory why most researchers go for readily available hardware for study. The most common choice is the widely available QWERTY keyboard BIB001 BIB002 , followed by built-in laptop keyboard BIB003 BIB005 . Some research works, unlike others, only used specific portion of a hardware BIB006 . The research restricted user to use num-pad of a keyboard with just one finger to replicate an impoverished experimental condition. They believed that if good result was achieved in such simplistic provision, then implementation in a less restrictive environment could likely accomplish better performance. On the other hand, BIB004 utilized Synaptic Touchpad attached to a notebook to measure finger pressure and position. Their intention was to implement keystroke dynamics biometrics on touch screen mobile devices, but due to the technology bottleneck at that point of time, it is understood why a cheaper alternative had been chosen. Although the device sensitivity might not be anywhere comparable to a real touch screen technology, the idea was inspirational for researchers when the technology becomes available.