A Survey of Keystroke Dynamics Biometrics <s> Dwell Time (DT). <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> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Dwell Time (DT). <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> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Dwell Time (DT). <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> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Dwell Time (DT). <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> BIB004
Dwell time refers to the amount of time between pressing and releasing a single key. In other words, how long a key was held pressing down. It is also worth noticing that several terms for DT appeared in the literature such as duration time BIB002 BIB003 and hold time BIB004 BIB001 . DT can be calculated by where and indicate the time stamp of release and press of a character, respectively, while indicates the position of the intended DT. For instance, referring to Figure 7 , DT for character " " and " " is 100 (200-100) and 250 (750-500) correspondingly. The total number of timing vector of DT ( DT ) that can be generated as follow: where denotes the summation of characters in a string. In other words, the number of DT generated will always be the same as the length of a given string.