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:  Dynamic Authentication Mode. 
1. Abstract of Cited Paper (BIB001):  This paper presents a novel biologic verification method based on pressure sensor keyboards and classifier fusion techniques. The pressure sensor keyboard is a new product that occurs in the market recently. It produces a pressure sequence when keystroke occurs. The analysis of the pressure sequence should be a novel research area. In this paper, we use the pressure sequence and traditional keystroke dynamics in user authentication. Three methods (global features of pressure sequences, dynamic time warping, and traditional keystroke dynamics) are proposed for the authentication task. We combined the three methods together using a classifier fusion technique at last. Several experiments were performed on a database containing 5000 samples of 100 individuals and the best result were achieved utilizing all the method, obtaining an equal error rate of 1.41%. To make a comparison, the equal error rate is 2.04% when we use only the traditional keystroke dynamics. This approach can be used to improve the usual login-password authentication when the password is no more a secret 
2. Abstract of Cited Paper (BIB002):  It is well known that Keystroke Dynamics can be used as a biometric to authenticate users. But most work to date use fixed strings, such as userid or password. In this paper, we study the feasibility of using Keystroke Dynamics as a biometric in a more general setting, where users go about their normal daily activities of emailing, web surfing, and so on. We design two classifiers that appropriate for one-time and continuous authentication. We also propose a new Goodness Measure to compute the quality of a word used for Keystroke Dynamics. From our experiments we find that, surprisingly, non-English words are better suited for identification than English words. 
3. Abstract of Cited Paper (BIB003):  This paper presents the development of a keystroke dynamics-based user authentication system using the ARTMAP-FD neural network. The effectiveness of ARTMAP- FD in classifying keystroke patterns is analyzed and compared against a number of widely used machine learning systems. The results show that ARTMAP-FD performs well against many of its counterparts in keystroke patterns classification. Apart from that, instead of using the conventional typing timing characteristics, the applicability of typing pressure to ascertaining user's identity is investigated. The experimental results show that combining both latency and pressure patterns can improve the equal error rate (ERR) of the system. 
4. Abstract of Cited Paper (BIB004):  Now mobile devices are developed to serve various functions, storing the sensitive information. In order to protect those information and mobile systems from unauthorized users, the authentication system must be installed unavoidably. Additionally, the development of the mobile system is moving forward to the touch screen system for user friendly and quick access mechanism. In this paper, we proposed behavioral manners of users over the touchpad acting like touch screen that is able to detect the finger pressure. These behaviors are keystroke dynamics and the finger pressure. The finding has shown that, the finger pressure gives the discriminative information more than keystroke dynamics with the k-NN analytical method. Moreover, using only the finger pressure produces high accuracy rate of 99%. 
5. Abstract of Cited Paper (BIB005):  Security of an information system depends to a large extent on its ability to authenticate legitimate users as well as to withstand attacks of various kinds. Confidence in its ability to provide adequate authentication is, however, waning. This is largely due to the wrongful use of passwords by many users. In this paper, the design and development of keystroke pressure-based typing biometrics for individual user's verification which based on the analysis of habitual typing of individuals is discussed. The paper examines the use of maximum pressure exerted on the keyboard and time latency between keystrokes as features to create typing patterns for individual users. Combining both an Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are adopted as classifiers to verify the authorized and unauthorized users based on extracted features of typing biometric. The effectiveness of the proposed system is evaluated based upon False Reject Rate (FRR) and False Accept Rate (FAR). A series of experiment shows that the proposed system that used combined classifiers produces promising result for both FAR and FRR. 
6. Abstract of Cited Paper (BIB006):  Now mobile devices are developed to serve various functions, storing the sensitive information. As a consequence, the authentication of users for mobile devices has become an important issue. Additionally, the development of the mobile system is moving forward to the touch screen system for user friendly and quick access mechanism. In this paper, we proposed behavioral manners of users over the touchpad acting like touch screen that is able to detect finger pressure. We study the potential of each biometrics behavioral by individual and couple, comprise with hold-time, inter-key and finger pressure. The finding has shown that, the finger pressure gives the discriminative information more than keystroke dynamics with the PNN analytical method. Moreover, using only the finger pressure produces high accuracy rate of 99%. 
7. Abstract of Cited Paper (BIB007):  Keystroke dynamics is unique specific characteristics used for user authentication problem. There are many researches to detect personal keystroke dynamics and authenticate user based on these characteristics. Most researches study on either the key press durations and multiple key latencies (typing time) or key-pressed forces (pressure-based typing) to find the owned personal motif (unique specific characteristic). This paper approaches to extract keystroke dynamics by using independent component analysis (ICA) through a standardized bio-matrix from typing sound signals which contain both typing time and typing force information. The ICA representation of keystroke dynamics is effective for authenticating user in our experiments. The experimental results show that the proposed keystroke dynamics extraction solution is feasible and reliable to solve user authentication problem with false acceptance rate (FAR) 4.12% and false rejection rate (FRR) 5.55%. 
8. Abstract of Cited Paper (BIB008):  The 2008 federal Higher Education Opportunity Act requires institutions of higher learning to make greater access control efforts for the purposes of assuring that students of record are those actually accessing the systems and taking exams in online courses by adopting identification technologies as they become more ubiquitous. To meet these needs, keystroke and stylometry biometrics were investigated towards developing a robust system to authenticate (verify) online test takers. Performance statistics on keystroke, stylometry, and combined keystroke-stylometry systems were obtained on data from 40 test-taking students enrolled in a university course. The best equal-error-rate performance on the keystroke system was 0.5% which is an improvement over earlier reported results on this system. The performance of the stylometry system, however, was rather poor and did not boost the performance of the keystroke system, indicating that stylometry is not suitable for text lengths of short-answer tests unless the features can be substantially improved, at least for the method employed. 
9. Abstract of Cited Paper (BIB009):  Cloud security is a major concern that may delay its widespread adoption. User access control (UAC) is the core component of security in cloud computing environment, aiming to ensure that stored data are allowed to be accessed only by authenticated/authorized users. As a typical behavioural biometrics, keystroke dynamics provides a promising UAC solution. The most challenging issue that hinders the wide deployment of keystroke is the high verification error rate. Gunetti et al. proposed a classical n-graph-based keystroke verification method (GP method), which can achieve a low False Acceptance Rate (FAR). However, the GP method suffers from a high False Rejection Rate (FRR) and a severe scalability issue. Thus, GP is not a feasible solution for computing cloud application where scalability is a big issue. In this paper, two keystroke verification approaches (nGdv-V and nGdv-C) are proposed to overcome GP's shortcomings. To reduce high FRR, we designed a new correlation measure using n-graph equivalent feature (nGdv) that enables more accurate recognition for genuine users. Moreover, correlation-based hierarchical clustering is proposed to address the scalability issue. The experimental results show that the nGdv-C can produce much lower FRR while achieving almost the same level of FAR as that of the GP method. Furthermore, 1250 times (when using nGdv-V) and three times (when using nGdv-C(17,4)) authentication speed gains have been achieved.

Literature Review:
In an effort toward developing a robust online examination authentication system, BIB009 investigated the use of not only keystroke feature but also stylometry. Stylometry was known as the study of recognizing authorship from the linguistic styles of an author. The k-nearest neighbour classifier has been applied. The experimental result shows that performance of traditional keystroke feature is superior to stylometry. This may be due to the operation of stylometry that depends heavily on words and syntax-level units; therefore, much longer text inputs are required for better recognition. Since dynamic authentication mode requires large amount of input text, BIB010 tried to utilized -graph feature timing vector instead of di-graph. A fairly straight forward displacement of each -graph sample pair of words are computed for distance measurement. One of the challenging scenarios of using -graphs in free text input is the need to collect the same -graphs for comparison. The flexibility of input text is essential to dynamic authentication. The immediate solution will be to gather as much typing inputs as possible, which translates into longer waiting time to collect enough keystrokes before authentication can effectively takes place. This might be the reason why majority of experiments have chosen di-graph for feature vector construction as shown in Table 6 . This observation was supported by BIB001 , where the experiments have been restricted to digraphs, tri-graphs, and four-graphs due to relative limited number of shared samples. On a side note, the author also pointed out that comparing sample over different typing languages will be possible provided the two languages shared same legal -graphs. A different approach was employed by BIB003 , whereby a set of fixed frequent appearing English words was used to form the basis of user typing reference template. At such, the wait for a word pattern to appear can be reduced whilst exploiting the stability of fixed text authentication in a free text environment. Due to the popularity of communication technologies such as instant messaging, online social networks chatting, and text messaging, the usage of non-English sequences (short hand notation and informal English abbreviations) has been increasingly dominance BIB003 . The research work proposed a goodness measure to quantify the quality of a series of fixed text based on the criteria of accuracy, availability, and universality of the text sequence. The author found that  The Scientific World Journal Table 7 . Remarkable result (EER ≤ 1%) has been obtained by BIB005 BIB006 BIB007 ; however, the number of subjects involved was too small (less than 10) to draw a strong conclusion. Conversely, although BIB004 reported a poorer result but 100 users participated in the experiment that might better reflect the scalability of the proposed method. By far the experiment that involved the largest test samples and yet achieved encouraging result (EER = 1.4) is BIB002 . The author constructed a feature vector that not only consisted of traditional timing vector but also the extraction of five global pressure attributes. Dynamic time warping, which has been commonly employed in speech and signature recognition was used to calculate the distance between pressure sequences. It is worth noticing that BIB008 demonstrated a very unique way of extracting keystroke pressure. The author proposed an indirect method to detect key-typed forces by analyzing sound signals generated upon striking on the keyboard with a sound recorder. Although without the need of pressure sensors attached to the keyboard would be an added advantage, the susceptibility to environmental noise may deter the quality of feature captured. By far none of the experiments utilized pressure sensitive screen on mobile device. Since we are stepping into the postpc era, smart phones and high-end tablet devices are commonly built-in with accurate pressure sensitive screens. It will be interesting to see how future research work corresponds with keystroke pressure feature by fully exploiting this readily available hardware technology.