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:  Feature Selection 
1. Abstract of Cited Paper (BIB001):  The authors have developed a software methodology that improves security by using typing biometrics to reinforce password authentication mechanisms. Typing biometrics is the analysis of a user's keystroke patterns. Each user has a unique way of using the keyboard to enter a password; for example, each user types the characters that constitute the password at different speeds. The methodology employs fuzzy logic to measure the user's typing biometrics. This reinforcement is transparent-indiscernible to the users while they are entering the normal authentication. 
2. Abstract of Cited Paper (BIB002):  Security is a critical component of most computer systems - especially those used in E-commerce activities over the Internet. Global access to information makes security a critical design issue in these systems. Deployment of sophisticated hardware based authentication systems is prohibitive in all but the most sensitive installations. What is required is a reliable, hardware independent and efficient security system. In this paper, we propose an extension to a keystroke dynamics based security system. We provide evidence that completely software based systems based on keystroke input dynamics can be as effective as expensive and cumbersome hardware based systems. Our system is behavioral based that captures the typing patterns of a user and uses that information, in addition to standard login/password security to provide a system that is user-friendly and very effective at detecting imposters. 
3. Abstract of Cited Paper (BIB003):  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. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  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%.

Literature Review:
Keystroke dynamics biometrics are rich with distinctive feature information that can be used for recognition purposes. Among the easiest and common feature harvested The Scientific World Journal by researchers is the timing measurement of individuals' keystroke inputs as shown in Figure 6 . Keystroke activity generates hardware interrupt that can be time stamped and measured up to microseconds (ms) precision BIB001 ; therefore, it can be readily applied. In previous works, timing resolution of 0.1 s to 1 ms has been deemed to be sufficient BIB004 . By performing simple mathematical operation to these time stamp, timing duration, or interval between consecutive keystrokes can be obtained. Several attempts, although uncommon, of using keystroke pressure, typing speed BIB003 , typing sequence difficulty BIB002 , frequency of typing error BIB005 , and sound of typing BIB006 have also been made. Due to the insignificant amount and unpopularity of the aforementioned feature type, the following subsections will focus on the discussion of the more popular timing feature.