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 Survey of Keystroke Dynamics Biometrics 
Chapter Title:  N-Graph 
1. Abstract of Cited Paper (BIB001):  Keystroke typing characteristics is considered as one of the important biometric features that can be used to protect users against malicious attacks. In this paper we propose a statistical model for web authentication with keystroke typing characteristics based on Hidden Markov Model and Gaussian Modeling from Statistical Learning Theory. Our proposed model can substantially enhance the accuracy of the identity authentication by analyzing keystroke timing information of the username and password. Results of the experiments showed that our scheme achieved by far the best error rate of 2.54%. 
2. Abstract of Cited Paper (BIB002):  Research in keystroke dynamics has largely focused on the typing patterns found in fixed text (e.g. userid and passwords). In this regard, digraphs and trigraphs have proven to be discriminative features. However, there is increasing interest in free-text keystroke dynamics, in which the user to be authenticated is free to type whatever he/she wants, rather than a pre-determined text. The natural question that arises is whether digraphs and trigraphs are just as discriminative for free text as they are for fixed text. We attempt to answer this question in this paper. We show that digraphs and trigraphs, if computed without regard to what word was typed, are no longer discriminative. Instead, word-specific digraphs/trigraphs are required. We also show that the typing dynamics for some words depend on whether they are part of a larger word. Our study is the first to investigate these issues, and we hope our work will help guide researchers looking for good features for free-text keystroke dynamics. 
3. Abstract of Cited Paper (BIB003):  The keystroke dynamics analysis is one of the biometric techniques which takes into account behavioral features. The difficulty with the keystroke dynamics information is its poor stability. This article briefly presents a solution used to collect training data and extract features for a user authentication system based on the keystroke dynamics. Then a few approaches which might be applied to authenticate users basing on the keystroke rhythm are presented. These approaches are going to be tested in order to choose an efficient method to be applied as a part of a biometric security system for mobile workstations, which is being created within the framework of the SART-2 project. 
4. Abstract of Cited Paper (BIB004):  Internet services are important part of daily activities for most of us. These services come with sophisticated authentication requirements which may not be handled by average Internet users. The management of secure passwords for example creates an extra overhead which is often neglected due to usability reasons. Furthermore, password-based approaches are applicable only for initial logins and do not protect against unlocked workstation attacks. In this paper, we provide a non-intrusive identity verification scheme based on behavior biometrics where keystroke dynamics based-on free-text is used continuously for verifying the identity of a user in real-time. We improved existing keystroke dynamics based verification schemes in four aspects. First, we improve the scalability where we use a constant number of users instead of whole user space to verify the identity of target user. Second, we provide an adaptive user model which enables our solution to take the change of user behavior into consideration in verification decision. Next, we identify a new distance measure which enables us to verify identity of a user with shorter text. Fourth, we decrease the number of false results. Our solution is evaluated on a data set which we have collected from users while they were interacting with their mail-boxes during their daily activities.

Literature Review:
. N-graph refers to the timing measurement between three or more consecutive keystroke events. It is better known as the elapse time between a key and the th key event of a typing string. Despite many combinations of elapse time (ET), it can be extracted; the equation below is the most widely used when -graph is concerned BIB002 BIB004 BIB001 . Consider where indicates the time stamp of pressing a character, denotes th number of graphs employed, while represents position of the intended elapse time. The total number of timing vector of ET exists in -graph which can be seen as follows: where denotes the summation of characters in a typing sequence. From this survey, we noticed that 80% used di-graph; 7% used tri-graph; only 4% used -graph, while 9% of the rest were unknown. The ability to generate significantly more instance of timing vectors could be the reason for the popularity of di-graph. As a result, any value of that is greater than 3 (tri-graph) was rarely chosen except for the experiment that involved huge amount of input text BIB005 BIB003 .