A Survey of Keystroke Dynamics Biometrics <s> Flight Time (FT). <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> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Flight Time (FT). <s> The keystroke dynamics of a computer user's login string provide a characteristic pattern that can be used for identity verification. Timing vectors for several hundred login attempts were collected for ten "valid" users and ten "forgers", and classification analysis was applied to discriminate between them. Three different classifiers were applied, and in each case the key hold times were more effective features for discrimination than the interkey times. Best performance was achieved by an inductive learning classifier using both interkey and hold times. A high rate of typographical errors during login entry is reported. In practice, these are usually corrected errors-that is, they are strings which include backspaces to correct earlier errors-but their presence confounds the use of typing-style analysis as a practical means of securing access to computer systems. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Flight Time (FT). <s> The major problem in the computer system is that users are now able to access data from remote places and perform transaction online. This paper reports on the experiment and performance of using keystroke dynamics as a user authentication method. The work is designed such that it is possible for the computer system to identify authorized and unauthorized user. This is desired to control access to a system that will assign the authorized user upon entering the system. The technique used to discriminate the data is Neural Network. This paper describes the application of neural networks to the problem of identifying specific users through the typing characteristics exhibited when typing their own name. The test carried out uses two kinds of neural network model, i.e. ADALINE and Backpropagation Network. A comparison of these two techniques are presented. <s> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Flight Time (FT). <s> Password typing is the most widely used identity verification method in Web based electronic commerce. Due to its simplicity, however, it is vulnerable to imposter attacks. Keystroke dynamics and password checking can be combined to result in a more secure verification system. We propose an autoassociator neural network that is trained with the timing vectors of the owner's keystroke dynamics and then used to discriminate between the owner and an imposter. An imposter typing the correct password can be detected with very high accuracy using the proposed approach. This approach can be effectively implemented by a Java applet and used for the Web. <s> BIB004 </s> A Survey of Keystroke Dynamics Biometrics <s> Flight Time (FT). <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. Password typing patterns or timing vectors of a user are measured and used to train a novelty detector model. However, without manual pre-processing to remove noises and outliers resulting from typing inconsistencies, a poor detection accuracy results. Thus, in this paper, we propose an automatic feature subset selection process that can automatically selects a relevant subset of features and ignores the rest, thus producing a better accuracy. Genetic algorithm is employed to implement a randomized search and SVM, an excellent novelty detector with fast learning speed, is employed as a base learner. Preliminary experiments show a promising result. <s> BIB005 </s> A Survey of Keystroke Dynamics Biometrics <s> Flight Time (FT). <s> Password is the most widely used identity verification method in computer security domain. However, because of its simplicity, it is vulnerable to imposter attacks. Use of keystroke dynamics can result in a more secure verification system. Recently, Cho et al. (J Organ Comput Electron Commerce 10 (2000) 295) proposed autoassociative neural network approach, which used only the user's typing patterns, yet reporting a low error rate: 1.0% false rejection rate (FRR) and 0% false acceptance rate (FAR). However, the previous research had some limitations: (1) it took too long to train the model; (2) data were preprocessed subjectively by a human; and (3) a large data set was required. In this article, we propose the corresponding solutions for these limitations with an SVM novelty detector, GA-SVM wrapper feature subset selection, and an ensemble creation based on feature selection, respectively. Experimental results show that the proposed methods are promising, and that the keystroke dynamics is a viable and practical way to add more security to identity verification. <s> BIB006 </s> A Survey of Keystroke Dynamics Biometrics <s> Flight Time (FT). <s> Keystroke dynamics-based authentication, KDA, verifies users via their typing patterns. To authenticate users based on their typing samples, it is required to find out the resemblance of a typing sample and the training samples of a user regardless of the text typed. In this paper, a measure is proposed to find the distance between a typing sample and a set of samples of a user. For each digraph, histogram-based density estimation is used to find the pdf of its duration time. This measure is combined with another measure which is based on the two samples distances. Experimental results show considerable decrease in FAR while FRR remains constant. <s> BIB007 </s> A Survey of Keystroke Dynamics Biometrics <s> Flight Time (FT). <s> Keystroke dynamics-based authentication (KDA) is to verify a user's identity using not only the password but also keystroke dynamics. With a small number of patterns available, data quality is of great importance in KDA applications. Recently, the authors have proposed to employ artificial rhythms and tempo cues to improve data quality: consistency and uniqueness of typing patterns. This paper examines whether improvement in uniqueness and consistency translates into improvement in authentication performance in real-world applications. In particular, we build various novelty detectors using typing patterns based on various strategies in which artificial rhythms and/or tempo cues are implemented. We show that artificial rhythms and tempo cues improve authentication accuracies and that they can be applicable in practical authentication systems. <s> BIB008 </s> A Survey of Keystroke Dynamics Biometrics <s> Flight Time (FT). <s> Traditionally user authentication is 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 for continuously verifying a user once the authentication process has successfully ended. However, current methods require long sessions and significant amounts of keystrokes to reliably verify users. We propose a new method that compactly represents the keystroke patterns by joining similar pairs of consecutive keystrokes. This automatically created representation reduces the session size required for inducing the user's verification model. The proposed method was evaluated on 21 legitimate users and 165 attackers. The results were encouraging and suggest that the detection performance of the proposed method is better than that of existing methods. Specifically we attained a false acceptance rate (FAR) of 3.47% and false rejection rate (FRR) of 0% using only 250 keystrokes. <s> BIB009
Flight time refers to the amount of time between pressing and releasing two successive keys. It may also be termed as latency time BIB003 BIB007 , interkey time BIB002  or interval time BIB009 BIB008 . It always involves key event (press or release) from two keys, wich could be similar or different characters. FT may exist in four different forms as depicted in Figure 7 . The formula to calculate each form are listed as follows: where and indicate the time stamp of release and press of a character, respectively, while indicates the position of the intended FT. As an example FT type1 between character " " and " " shown in Figure 7 is 300 (500-200), whereas the FT type3 is 400 (500-100). The previous literature pointed out the possibility of obtaining negative value (<0) for FT type1 BIB001 BIB004 BIB005 BIB006 . This situation occurs when an individual presses the next key before releasing the previous key. However, a closer observation shows that it is also possible for FT type2 to incur this property, albeit in a very exceptional circumstance. The total number of timing vector of FT ( FT ) that can be generated is shown as follows: where denotes the summation of characters in a string. Differing from DT, the number of FT generated will always be one less than the length of a given string.