A Survey of Keystroke Dynamics Biometrics <s> Device Freedom and Experimental <s> More than ever before the Internet is changing computing as we know it. Global access to information and resources is becoming an integral part of nearly every aspect of our lives. Unfortunately, with this global network access comes increased chances of malicious attack and intrusion. In an effort to confront the new threats unveiled by the networking revolution of the past few years reliable, rapid, and unintrusive means for automatically recognizing the identity of individuals are now being sought. In this paper we examine an emerging non-static biometric technique that aims to identify users based on analyzing habitual rhythm patterns in the way they type. ©2000 Elsevier Science B.V. All rights reserved. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Device Freedom and Experimental <s> Keystroke dynamics can be useful to ascertain personal identity even after an authentication phase has been passed, provided that we are able to deal with the typing rhythms of free text, chosen and entered by users without any specific constraint. In this paper we present a method to compare typing samples of free text that can be used to verify personal identity. We have tested our technique with a wide set of experiments on 205 individuals, obtaining a False Alarm Rate of less than 5p and an Impostor Pass Rate of less than 0.005p. Different trade-offs are, however, possible. Our approach can rely on what is typed by people because of their normal job, and a few lines of text, even collected in different working sessions, are sufficient to reach a high level of accuracy, which improves proportionally to the amount of available information: As a consequence, we argue that our method can be useful in computer security as a complementary or alternative way to user authentication and as an aid to intrusion detection. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Device Freedom and Experimental <s> This paper presents the study to develop and evaluate techniques to authenticate valid users, using the keystroke dynamics of a user's PIN number entry on a numerical keypad, with force sensing resistors. Added with two conventional parameter lists of elements, i.e. digraph latency times and key hold times, keying force was chosen as a third element. Two experiments were conducted. The first experiment was to evaluate whether the three types of elements derived from keystrokes have a significant effect for subjects and to examine how trials and session effects generated the variation of the three elements. The second experiment was to demonstrate the system performance by calculating the False Rejection Rate (FRR) and the False Acceptance Rate (FAR) of the system. In the second experiment, a total of 20 keystrokes were recorded from each subject one week after the memorizing session, in order to evaluate the FRR of the system. To evaluate the FAR of the system, the subjects pretended to be impostors, and ... <s> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Device Freedom and Experimental <s> Current keystroke dynamics applications have tackled the problem of traditional knowledge-based static password verification, but the problem of spontaneous password verification persists. The intent of this study was to examine the predictive strength of typing patterns for spontaneous passwords. The typing patterns of an individual typing at a DELLreg keyboard on a DELL OptiPlexreg GX260 machine were recorded. Variables collected included keystroke press time and keystroke latency. Computed performance measures included false match rates (FMR) and false non match rates (FNMR) at various threshold levels <s> BIB004 </s> A Survey of Keystroke Dynamics Biometrics <s> Device Freedom and Experimental <s> Keystroke dynamics is the process of identifying individual users on the basis of their typing rhythms, which are in turn derived from the timestamps of key-press and key-release events in the keyboard. Many researchers have explored this domain, with mixed results, but few have examined the relatively impoverished territory of digits only, particularly when restricted to using a single finger - which might come into play on an automated teller machine, a mobile phone, a digital telephone dial, or a digital electronic security keypad at a building entrance. In this work, 28 users typed the same 10-digit number, using only the right-hand index finger. Employing statistical machine-learning techniques (random forest), we achieved an unweighted correct-detection rate of 99.97% with a corresponding false-alarm rate of 1.51%, using practiced 2-of-3 encore typing with outlier handling. This level of accuracy approaches sufficiency for two-factor authentication for passwords or PIN numbers. <s> BIB005 </s> A Survey of Keystroke Dynamics Biometrics <s> Device Freedom and Experimental <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 </s> A Survey of Keystroke Dynamics Biometrics <s> Device Freedom and Experimental <s> Keystroke dynamics—technology to distinguish people based on their typing rhythms—could revolutionize insider-threat detection. Insiders accessing backdoors, using shared accounts, or masquerading as other users would be exposed by their unique typing rhythms. In the past thirty years, dozens of classifiers have been proposed for distinguishing people using keystroke dynamics; many have obtained excellent results in evaluation. However, when evaluations are replicated, the results are often wildly different; one classifier's error rate jumped from 1 % to 85% upon replication. ::: Classifier error rates depend on a multitude of factors; until the effects of these factors on error rates are understood, keystroke dynamics cannot realize its promise. To tackle this multitude-of-factors problem, we developed the following methodology: (I) evaluate multiple classifiers under systematically ranging conditions; (2) analyze the results with linear mixed-effects models (LMMs), a technique for inferential statistics well suited to understanding how various factors affect classifier error rates; and (3) validate the models, demonstrating that they accurately predict error rates in subsequent evaluations. ::: In three investigations using this methodology, we found that while some classifiers had lower error rates than others, the differences were overshadowed by the effects of factors other than the classifier. For the best classifier, error rates vary from 0% to 63% depending on the user. Impostors triple their chance of evading detection if they touch type. On the bright side, the best combination of timing features (hold times and up-down times) reduces error rates by over 50%. Other configuration tweaks, such as increased training and an updating strategy, offer further opportunity to significantly reduce error rates. On a scientific level, this work establishes that understanding these factors is critical to making progress in keystroke dynamics. ::: By understanding the influential factors, we can deploy the best classifier given the environment, accurately estimate its error rate, and know where to direct future efforts to improve performance. For the first time in keystroke dynamics, we can reliably predict classifier error rates, and our approach is general. Since other computer-security technologies (e.g., intrusion, worm, and malware detection) face analogous multitude-of-factors problems, they would similarly benefit from our methodology. <s> BIB007
Control. Device freedom refers to whether the equipment used in the experiments is standardized or the users have the flexibility to use their own input devices. Among approximately 187 publications surveyed, 34% used predefine standard device, 17% performed experiment on user's own device, while the remaining 49% were unknown due to inadequate information. However, The Scientific World Journal it is reasonable to assume that they employ fixed devices strategy since those experiments that allow user to make use of their own devices often mentioned explicitly. The fixed setting can get rid of introducing uncontrollable variables such as device familiarity, device compatibility, and functional differences hence, the result is solely reflected by the discriminative power of keystroke dynamics feature or classification algorithm BIB005 BIB004 . The rationale behind this thought is that the user may be more accustomed to their own input devices that may lead to distortion of experimental data. Although some may not clearly state this information, it is no doubt that experiments that use customized devices (e.g., pressure sensitive keyboard) were provided by the researchers. This might be the reason why it is in favor by the most researchers, almost twice the amount compared to user centric devices. In contrast, some research works employed different approaches by not restricting the usage of device. For instance, BIB001 requested user to download an executable program into their own personal computer for data collection, while BIB002 implemented a JavaScript web-based client, where users are free to use any machine as long as it comes with a web browser. At such, it can be argued that the experimental results obtained closely resembled real world scenario. Another vital variable is the constraints that researchers imposed particularly in data collection phase. Experiments may be conducted entirely in a supervised environment with a strict protocol such as in BIB003 . Video clips of legitimate user login trials are prerecorded and later presented to the imposter in an attempt to imitate genuine user login during testing stage. Apart from that, experiments that involved additional customized hardware  or software library BIB005 will apparently be best to be performed under controlled laboratory environment. At such, the hassle and complexity of experimental deployment as well as the cost of implementation can be kept minimal. It was also argued by BIB006 that one of the benefits of operating experiments under stringent protocol is to single out external factor from inflicting noise. As a result, primary experimental variables could be clearly evaluated BIB007 . However, there may be a concern that the result obtained under such control setting may not reflect real world scenario. On the contrary, experiments that did not impose restriction or unmonitored offered user comfort and flexibility that resembled realistic condition. As an example, the nature of the experiment conducted by BIB002 required the collection of typing pattern of user daily activity on a computer. Data collected by allowing user to use their preferential device is more desirable than requiring user to work on an entirely unfamiliar device. Since lacking of constraints, the quality of data collected could be distorted or tempered with. Perhaps these might be the reasons why most research works perform under close administration, more than double of the amount of those uncontrolled.