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:  Outlier Handling. 
1. Abstract of Cited Paper (BIB001):  The variables that help make a handwritten signature a unique human identifier also provide a unique digital signature in the form of a stream of latency periods between keystrokes. This article describes a method of verifying the identity of a user based on such a digital signature, and reports results from trial usage of the system. 
2. Abstract of Cited Paper (BIB002):  Password typing is the most widely used identity verification method in Web based electronic commerce. Due to its simplicity, however, it is vulnerable to imposter attacks. Keystroke dynamics and password checking can be combined to result in a more secure verification system. We propose an autoassociator neural network that is trained with the timing vectors of the owner's keystroke dynamics and then used to discriminate between the owner and an imposter. An imposter typing the correct password can be detected with very high accuracy using the proposed approach. This approach can be effectively implemented by a Java applet and used for the Web. 
3. Abstract of Cited Paper (BIB003):  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. Password typing patterns or timing vectors of a user are measured and used to train a novelty detector model. However, without manual pre-processing to remove noises and outliers resulting from typing inconsistencies, a poor detection accuracy results. Thus, in this paper, we propose an automatic feature subset selection process that can automatically selects a relevant subset of features and ignores the rest, thus producing a better accuracy. Genetic algorithm is employed to implement a randomized search and SVM, an excellent novelty detector with fast learning speed, is employed as a base learner. Preliminary experiments show a promising result. 
4. Abstract of Cited Paper (BIB004):  Password is the most widely used identity verification method in computer security domain. However, because of its simplicity, it is vulnerable to imposter attacks. Use of keystroke dynamics can result in a more secure verification system. Recently, Cho et al. (J Organ Comput Electron Commerce 10 (2000) 295) proposed autoassociative neural network approach, which used only the user's typing patterns, yet reporting a low error rate: 1.0% false rejection rate (FRR) and 0% false acceptance rate (FAR). However, the previous research had some limitations: (1) it took too long to train the model; (2) data were preprocessed subjectively by a human; and (3) a large data set was required. In this article, we propose the corresponding solutions for these limitations with an SVM novelty detector, GA-SVM wrapper feature subset selection, and an ensemble creation based on feature selection, respectively. Experimental results show that the proposed methods are promising, and that the keystroke dynamics is a viable and practical way to add more security to identity verification. 
5. Abstract of Cited Paper (BIB005):  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. 
6. Abstract of Cited Paper (BIB006):  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. 
7. Abstract of Cited Paper (BIB007):  A long-text-input keystroke biometric system was developed for applications such as identifying perpetrators of inappropriate e-mail or fraudulent Internet activity. A Java applet collected raw keystroke data over the Internet, appropriate long-text-input features were extracted, and a pattern classifier made identification decisions. Experiments were conducted on a total of 118 subjects using two input modes - copy and free-text input - and two keyboard types - desktop and laptop keyboards. Results indicate that the keystroke biometric can accurately identify an individual who sends inappropriate email (free text) if sufficient enrollment samples are available and if the same type of keyboard is used to produce the enrollment and questioned samples. For laptop keyboards we obtained 99.5% accuracy on 36 users, which decreased to 97.9% on a larger population of 47 users. For desktop keyboards we obtained 98.3% accuracy on 36 users, which decreased to 93.3% on a larger population of 93 users. Accuracy decreases significantly when subjects used different keyboard types or different input modes for enrollment and testing. 
8. Abstract of Cited Paper (BIB008):  User authentication based on keystroke dynamics is concerned with accepting or rejecting someone based on the way the person types. A timing vector is composed of the keystroke duration times interleaved with the keystroke interval times. Which times or features to use in a classifier is a classic feature selection problem. Genetic algorithm based wrapper approach does not only solve the problem, but also provides a population of fit classifiers which can be used in ensemble. In this paper, we propose to add uniqueness term in the fitness function of genetic algorithm. Preliminary experiments show that the proposed approach performed better than two phase ensemble selection approach and prediction based diversity term approach. 
9. Abstract of Cited Paper (BIB009):  As information and network security is exposing to increasing evil threats significantly, authentication is now playing a more and more important role in security defense system. In this paper, a model that authenticates and identifies access to computer systems and networks by applying keystroke manner is put forward. In the model, legitimate user's typing manner such as durations of keystrokes and latencies between keystrokes are collected during training step, and several features are extracted from the keystroke data. Then user's manner template of keystroke is built by principal component analysis. While identifying, some principal component scores of user data are used to judge the user's validity. The procedure of authentication is invisible to user. The results of related experiment show that the model has a good discerning ability. 
10. Abstract of Cited Paper (BIB010):  Keystroke authentication is a biometric method utilizing the typing characteristics of users. In this paper, we propose an evolutionary method for stable keystroke authentication. In the method, typing characteristics of users are represented by n-dimensional vectors and an ellipsoidal hypothesis space, which distinguishes a collection of the timing vectors of a user from those of the others, is evolved by a genetic algorithm. A filtering scheme and an adaptation mechanism are also presented to improve the stability and effectiveness of the proposed method. Empirical results show that the error rates of our method for authentication are reasonably small. 
11. Abstract of Cited Paper (BIB011):  The keystroke patterns produced during typing have been shown to be unique biometric signatures. Therefore, these patterns can be used as digital signatures to verify the identity of computer users remotely over the Internet or locally at a specific workstation. In particular, keystroke recognition can enhance the username and password security model by monitoring the way that these strings are typed. To this end, this paper proposes a novel up--up keystroke latency (UUKL) feature and compares its performance with existing features using a Gaussian mixture model (GMM)-based verification system that utilizes an adaptive and user-specific threshold based on the leave-one-out method (LOOM). The results show that the UUKL feature significantly outperforms the commonly used key hold-down time (KD) and down--down keystroke latency (DDKL) features. Overall, the inclusion of the UUKL feature led to an equal error rate (EER) of 4.4% based on a database of 41 users, which is a 2.1% improvement as compared to the existing features. Comprehensive results are also presented for a two-stage authentication system that has shown significant benefits. Lastly, due to many inconsistencies in previous works, a formal keystroke protocol is recommended that consolidates a number of parameters concerning how to improve performance, reliability, and accuracy of keystroke-recognition systems. 
12. Abstract of Cited Paper (BIB012):  The use of stolen personal-identifying information, like Social Security numbers, to commit identity fraud continues to be a major problem. The fact that an impostor can pass as the genuine user by possession of stolen identification information is a weakness in current authentication systems. Adding a biometric layer to the traditional knowledge and token-based authentication systems is one way to counter this problem. Physical biometrics, such as fingerprint systems, are highly accurate; hence, they would be the first choice for such applications but are often inappropriate. Behavioral biometrics, like biometric typing patterns, have the potential to fill this gap as another level of security but this research identified some deficiencies in performance quality. Two research streams for improvements have emerged. The first approach attempts to improve performance by building better classifiers, while the second attempts to attain the same goal by using richer identifying inputs. Both streams assume that the typing biometric patterns are stable over time. This study investigates the validity of this assumption by analyzing how students’ typing patterns behave over time. The results demonstrate that typing patterns change over time due to learning resulting in several performance quality challenges. First, the changing patterns lead to deteriorating authentication accuracy. Second, the relevancy of the reference biometric template created during training becomes questionable. Third, the deterioration in accuracy compromises the security of the whole system and fourth, the net effect brings to question whether the biometric keypad is no longer “fit for use” as an authentication system. These are critical data quality issues that need to be addressed if behavioral biometrics are to play a significant role in minimizing authentication fraud. Possible solutions to the problem, including biometric template updating and choice of uncorrelated PIN combinations, are suggested as potential topics for future research. 
13. Abstract of Cited Paper (BIB013):  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.

Literature Review:
Outlier is an atypical or extreme data that is significantly out of norm. For instance, a keystroke timing value of 3000 ms would likely be considered as outlier, since the mean range of probable human keystroke timing value is between 96 to 825 ms BIB012 . The origin of noise in data may be initiated by random pauses or hesitations BIB001 or physical state of user or environmental condition BIB011 that disturbs user typing and could skew the feature measurements BIB008 . Such outlier, if not specially handled, may affect classification outcome and consequently degrades system performance. Noise removal BIB004 , data cleaning BIB005 , or extreme outlier removal BIB012 might lead to better performance as claimed by BIB013 BIB008 BIB009 . Several methods of outlier handling exist in the literature, where an adjustable constant is the most common BIB011 BIB007 BIB014 BIB002 . The following inequality describes the elimination condition: where refers to a timing value instance, represents an adjustable constant, and is the standard deviation of a reference template. Timing value will be removed if (7) is not met. A large value of indicates that more timing value will be discarded as training sample or may also imply that a user did not type consistently [20] . Nevertheless, precaution should be taken during the establishment of discarding threshold so that the remaining number of samples is not too small for training. Another similar approach taken by BIB006 BIB003 is by removing the outlier if any of the value deviated from the upper or lower of a predetermined percentage (e.g., 10%). Kaneko et al.  used an empirical fixed value (e.g., 240 ms) as determination criteria on detecting noisy data. This method might not be scalable since outlier is dissimilar for different individuals due to diverse typing proficiency. Other methods such as f-distribution BIB013 and principle component analysis BIB010 have also been explored. Human judgement on inconsistency of data is subjective and may be dissimilar among different persons BIB005 . Furthermore, manual outlier detection and removal is infeasible in an automated system. Thus, BIB004 proposed using Genetic Algorithm-Support Vector Machine that can automatically select the relevant subset of feature and disregard noisy data without human intervention. Although evidences in the literatures show that removal of outlier generally results in better performance, it may reduce training data samples. As compensation, significant effort has to be put to collect larger data sample.