You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A review on the public benchmark databases for static keystroke dynamics 
Chapter Title:  Introduction 
1. Abstract of Cited Paper (BIB001):  Most personal identity mechanisms in use today are artificial. They require specific actions on the part of the user, many of which are not “friendly”. Ideally, a typist should be able to approach a computer terminal, begin typing, and be identified from keystroke characteristics. Individuals exhibit characteristic cognitive properties when interacting with the computer through a keyboard. By examining the properties of keying patterns, statistics can be compiled that uniquely describe the user. Initially, a reference profile is built to serve as a basis of comparison for future typing samples. The profile consists of the average time interval between keystrokes (mean keystroke latency) as well as a collection of the average times required to strike any two successive keys on the keyboard. Typing samples are scored against the reference profile and a score is calculated assessing the confidence that the same individual typed both the sample and the reference profile. This mechanism has the capability of providing identity surveillance throughout the entire time at the keyboard. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  Fusing the scores of several biometric systems is a very promising approach to improve the overall system's accuracy. Despite many works in the literature, it is surprising that there is no coordinated effort in making a benchmark database available. It should be noted that fusion in this context consists not only of multimodal fusion, but also intramodal fusion, i.e., fusing systems using the same biometric modality but different features, or same features but using different classifiers. Building baseline systems from scratch often prevents researchers from putting more efforts in understanding the fusion problem. This paper describes a database of scores taken from experiments carried out on the XM2VTS face and speaker verification database. It then proposes several fusion protocols and provides some state-of-the-art tools to evaluate the fusion performance. 
4. Abstract of Cited Paper (BIB004):  Keystroke dynamics based authentication (KDA) verifies a user based on the typing pattern. During enroll, a few typing patterns are provided, which are then used to train a classifier. The typing style of a user is not expected to change. However, sometimes it does change, resulting in a high false reject. In order to achieve a better authentication performance, we propose to continually retrain classifiers with recent login typing patterns by updating the training data set. There are two ways to update it. The moving window uses a fixed number of most recent patterns while the growing window uses all the new patterns as well as the original enroll patterns. We applied the proposed method to the real data set involving 21 users. The experimental results show that both the moving window and the growing window approach outperform the fixed window approach, which does not retrain a classifier. 
5. Abstract of Cited Paper (BIB005):  In keystroke dynamics-based authentication, novelty detection methods are used since only the valid user's patterns are available when a classifier is first constructed. After a while, however, impostors' keystroke patterns become available from failed login attempts. We propose to employ the retraining framework where a novelty detector is retrained with the impostor patterns to enhance authentication accuracy. In this paper, learning vector quantization for novelty detection and support vector data description are retrained with the impostor patterns. Experimental results show that retraining improves the authentication performance and that learning vector quantization for novelty detection outperforms other widely used novelty detectors. 
6. Abstract of Cited Paper (BIB006):  Our daily interactions are based on recognizing people. As information technology advances, our interactions will be accomplished through newly designed interfaces, with a vital element being the automated process of recognition of a persons real identity: Biometrics. In recent years, biometrics has moved from using fingerprints, to many different methods of assessing human physical and behavioural traits. This useful guide introduces a new performance evaluation framework designed to provide complete coverage of performance evaluation of major biometric systems. Two key areas are presented: The first gives a unique snapshot of the performance evaluation of various biometrics within a common evaluation framework (databases and protocols). The second presents a state-of-the-art benchmarking evaluation framework for use in future performance evaluations comprised of open-source reference systems. Features and benefits: Includes a Foreword by renowned biometric expert Prof. Anil K. Jain, Michigan State University, USA Introduces the concept of reproducible results and comparative evaluations Presents an extensive benchmarking methodology Describes reference algorithms in detail, with a link to source code Biometrics Reference and Evaluation Toolkit provided via http://share.int-evry.fr/svnview-eph/ Implements this methodology throughout all chapters Offers readers a tool for a better evaluation of their algorithms by giving them the instruments to fully reproduce the benchmarking (reference) experiments (software, database, experimental protocols) Provides a global perspective with a common evaluation scheme and methodology Examines current research results with this framework Consolidates results from publicly available databases, using well defined protocols This unique text/reference combines state-of-the-art research with practical tools for researchers, practitioners and graduates in the field of Biometrics and Pattern Recognition, and with its comprehensive coverage, it will prove an indispensable resource and frequently used tool. Key topics BioSecure Benchmarking Methodology Biometric Performance Evaluation Iris Recognition Fingerprint Recognition Hand Recognition Online Handwritten Signature Verification Text-independent Speaker Verification 2D and 3D Face Recognition Talking Face Verification 
7. Abstract of Cited Paper (BIB007):  Everybody needs to authenticate himself on his computer before using it, or even before using different applications (email, e-commerce, intranet, . . . ). Most of the times, the adopted authentication procedure is the use of a classical couple of login and password. In order to be efficient and secure, the user must adopt a strict management of its credentials (regular changing of the password, use of different credentials for different services, use of a strong password containing various types of characters and no word contained in a dictionary). As these conditions are quite strict and difficult to be applied for most users, they do not not respect them. This is a big security flaw in the authentication mechanism (Conklin et al., 2004). According to the 2002 NTA Monitor Password Survey1, a study done on 500 users shows that there is approximately 21 passwords per user, 81% of them use common passwords and 30% of them write their passwords down or store them in a file. Hence, password-based solutions suffer from several security drawbacks. A solution to this problem, is the use of strong authentication.... 
8. Abstract of Cited Paper (BIB008):  Keystroke dynamics is a behavioral biometrics showing a degradation of performance when used over time. This is due to the fact that the user improves his/her way of typing while using the system, therefore the test samples may be different from the initial template computed at an earlier stage. One way to bypass this problem is to use template update mechanisms. We propose in this work, new semi-supervised update mechanisms, inspired from known supervised ones. These schemes rely on the choice of two thresholds (an acceptance threshold and an update threshold) which are fixed manually depending on the performance of the system and the level of tolerance in possible inclusion of impostor data in the update template. We also propose a new evaluation scheme for update mechanisms, taking into account performance evolution over several time-sessions. Our results show an improvement of 50% in the supervised scheme and of 45% in the semi-supervised one with a configuration of the parameters chosen so that we do not accept many erroneous data. 
9. Abstract of Cited Paper (BIB009):  Among all the existing biometric modalities, authentication systems based on keystroke dynamics present interesting advantages. These solutions are well accepted by users and cheap as no additional sensor is required for authenticating the user before accessing to an application. In the last thirty years, many researchers have proposed, different algorithms aimed at increasing the performance of this approach. Their main drawback lies on the large number of data required for the enrollment step. As a consequence, the verification system is barely usable, because the enrollment is too restrictive. In this work, we propose a new method based on the Support Vector Machine (SVM) learning satisfying industrial conditions (i.e., few samples per user are needed during the enrollment phase to create its template). In this method, users are authenticated through the keystroke dynamics of a shared secret (chosen by the system administrator). We use the GREYC keystroke database that is composed of a large number of users (100) for validation purposes. We compared the proposed method with six methods from the literature (selected based on their ability to work with few enrollment samples). Experimental results show that, even though the computation time to build the template can be longer with our method (54 s against 3 s for most of the others), its performance outperforms the other methods in an industrial context (Equal Error Rate of 15.28% against 16.79% and 17.02% for the two best methods of the state-of-the-art, on our dataset and five samples to create the template, with a better computation time than the second best method).

Literature Review:
Keystroke dynamics BIB008 is a behavioural biometric modality, which allows the authentication of individuals through their way of typing their password on a keyboard. It is a behavioural biometrics which presents the advantage of not requiring additional sensor than the keyboard at hand and which allows authentication through time (on-line authentication). It is probably the cheapest biometric modality available of a personal computer. Biometric systems are validated thanks to datasets that are collected for this purpose and can be qualified in function of the problem that one wants to solve. For instance, if one wants to benchmark algorithms for face recognition, the algorithms may give very different performance on a dataset which contains no variability (visage is frontal with no expression and no illumination variation) and on other dataset which contains some of these variabilities. The choice of the dataset is therefore guided by the precise problem the algorithm aims at solving. A good evaluation framework corresponds to the association of a reference system, a dataset and an associated evaluation protocol which fixes the way the dataset is used. This way, it is possible to compare different systems between them without providing any bias and to report comparative results using some error measures which very often in biometrics corresponds to EER and ROC curves BIB004  . Such protocols have been proposed for most of the biometric modalities as described in BIB007 . Keystroke dynamics is a recent modality for which no such framework has been yet proposed. One of the aims of the present work is to give some report of the existing situation in terms of publicly available datasets and to suggest some criteria that could guide the construction of new datasets helpful for the research community. Although the authentication is done in real time (i.e., online) in a real world system, scientists working on keystroke dynamics do not analyze the performance of their system online (i.e, by asking users to authenticate themselves in real time and to impersonate other users). Indeed, they work in an offline way by using samples previously collected, probably by other researchers, and stored in a benchmark dataset. Thus, data collection and algorithms evaluation are often two separated tasks. The evaluation of algorithms is eased thanks to this offline procedure. If scientists share the same common benchmark, they can fairly compare their algorithms by considering error results and time consumption. Experiments become easily reproducible, which is an important criterion in scientific studies (although datasets could contain errors). Data collection is a tedious and time consuming task, which can explain why there are only few benchmark datasets of high quality. It is important to characterize the datasets in order to be able to easily compare them, and to choose the adequate one depending on the study. A keystroke dynamic (KD) system (KDS) is composed of two main modules: the enrollment and the verification modules. Each user must enroll himself in the KDS which computes a biometric reference given multiple samples (i.e., several inputs of the password) acquired during the enrollment step. For each input, a sequence of timing information is captured (i.e., time when each key is pressed or released) from which some features are extracted (i.e., latencies and durations) and used to learn the model which characterizes each user. During a verification request, the claimant types his/her password. The system extracts the features and compares them to the biometric reference of the claimant. If the obtained distance is below a threshold, the user is accepted, otherwise he/she is rejected. An optional module can be used to automatically update the model of the user BIB009 . This can be important, as the KD data is not permanent and evolves with time BIB009 BIB005 . First works on KD have been done in the eighties BIB001 , although the idea of using a keyboard to automatically identify individuals has first been presented in 1975  . In the preliminary report of Gaines et al. BIB001 , seven secretaries typed several paragraphs of text and researchers showed that it is possible to differentiate users with their typing patterns. Since then, several studies have been done, allowing to decrease the quantity of information needed to build the biometric reference, while improving the performances BIB002 BIB003  BIB006 BIB010 . However, most studies are not comparable because they use different datasets or protocols BIB010  . In this paper, we present a review of existing publicly accessible benchmark datasets for keystroke dynamics. We also propose a methodology to characterize these KD benchmark datasets (KDB). Although, it would be fruitful to rank them according to some criteria, it is actually difficult to give them a score because such score cannot be generic, as it would depend on the kind of study we want to do, and would be based on too many subjective points. Consequently, in this work, we aim at considering all the interesting points to analyze in order to qualify a KDB; using this information, the scientist will be able to choose the best dataset adapted to his particular experiment. Alternatively, it can also help him to create a new dataset of higher quality than the existing ones and maybe more suitable to the problem he wants to tackle with. In this work, we focus only on static password KD authentication systems (i.e., each user is authenticated thanks to the typing of an expected password, and not free text). The originality of this work is (a) the proposition of a complete set of criteria for characterizing different datasets, which is lacking in this field, and (b) the analysis of all the existing public KDB with respects to these criteria. We think this work is important, because it is known that KD studies are not fair as (i) acquisition protocols are different between studies BIB010 ; (ii) there is not always a comparative study  when authors propose new algorithms; and (iii) there are not always a valuable statistical evaluation  . Our work helps to solve the two first problems, while methods presented in [15] can solve the third one. The paper is organized as follows. Section 2 presents a review of existing benchmark datasets for keystroke dynamics. Section 3 presents the various important elements to consider when characterizing KDB. Section 4 realizes a comparative study of public KDB by using all the criteria defined in the previous sections. Section 5 concludes this paper.