A Survey of Keystroke Dynamics Biometrics <s> Subject Demographic. <s> Abstract : The growing use of computers to store sensitive, private, and classified information makes it increasingly important to be able to determine with a very high degree of confidence the identity of an individual seeking access to the computer. This report summarizes preliminary efforts to establish whether an individual can be identified by the statistical characteristics of his or her typing. Can people be identified by the way they type? To investigate this question, an experiment was carried out at Rand, in which seven professional typists were each given a paragraph of prose to type, and the times between successive keystrokes were recorded. This procedure was repeated 4 months later with the same typists and the same paragraph of prose. By examining the probability distributions of the times each typist required to type certain pairs of successively typed letters (digraphs), the authors found that of the large number of digraphs represented in most ordinary paragraphs, there were five which, considered together, could serve as a basis for distinguishing among the subjects. The implications of this finding are that touch typists appear to have a typing "signature," and that this method of distinguishing subjects might provide the basis for a computer authentication system. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Subject Demographic. <s> Smart phones are now being used to store users' identities and sensitive information/data. Therefore, it is important to authenticate legitimate users of a smart phone and to block imposters. In this paper, we demonstrate that keystroke dynamics of a smart phone user can be translated into a viable features' set for accurate user identification. To this end, we collect and analyze keystroke data of 25 diverse smart phone users. Based on this analysis, we select six distinguishing keystroke features that can be used for user identification. We show that these keystroke features for different users are diffused and therefore a fuzzy classifier is well-suited to cluster and classify them. We then optimize the front-end fuzzy classifier using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as back-end dynamic optimizers to adapt to variations in usage patterns. Finally, we provide a novel keystroke dynamics based PIN (Personal Identification Number) verification mode to ensure information security on smart phones. The results of our experiments show that the proposed user identification system has an average error rate of 2% after the detection mode and the error rate of rejecting legitimate users drops to zero in the PIN verification mode. We also compare error rates (in terms of detecting both legitimate users and imposters) of our proposed classifier with 5 existing state-of-the-art techniques for user identification on desktop computers. Our results show that the proposed technique consistently and considerably outperforms existing schemes. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Subject Demographic. <s> This paper uses a static typing biometrics in user authentication. The inputs are the key down and up times and the key ASCII codes captured while the user is typing a string. Four features (key code, two keystroke latencies and key duration) were analyzed, and, seven experiments were performed combining these features. The results of the experiments were evaluated involving three types of user: the legitimate, the impostor and the observer impostor users. The best results were achieved utilizing all features, obtaining a false rejection rate (FRR) of 1.45% and a false acceptance rate (FAR) of 1.89%. This approach can be used to improve the usual login-password authentication when the password is no more a secret. This paper innovates using the combination of four features to authenticate users. <s> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Subject Demographic. <s> 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. <s> BIB004 </s> A Survey of Keystroke Dynamics Biometrics <s> Subject Demographic. <s> The rise of the Internet and the push for ubiquitous computing has brought a proliferation of numerous single method solutions, forcing users to remember numerous secret codes, a task that is becoming increasingly difficult. On the web, codes are used by publications, blogs, Webmail, e-commerce sites, and financial institutions. Elsewhere, they serve as authentication mechanism for Internet service providers (ISPs), email servers, local and remote host account, ATM, voicemails and so on. Existing textual passwords, token based systems, and other methods often do not offer the necessary security standard. Fortunately biometric systems that are based on the biological features of the user when typing texts are very promising in enhancing the de facto textual password. The main objective of this paper is to assess and report on the suitability of keystroke dynamics in protecting access to resources when users are typing the different types of password that exist. <s> BIB005 </s> A Survey of Keystroke Dynamics Biometrics <s> Subject Demographic. <s> abstract Keystroke dynamics are becoming a well-known method for strengthening username- and password-based credential sets. The familiarity and ease of use of these traditional authentication schemes combined with the increased trustworthiness associated with biometrics makes them prime candidates for application in many web-based scenarios. Our keystroke dynamics system uses Breiman’s random forests algorithm to classify keystroke input sequences as genuine or imposter. The system is capable of operating at various points on a traditional ROC curve depending on application-specific security needs. As a username/password authentication scheme, our approach decreases the system penetration rate associated with compromised passwords up to 99.15%. Beyond presenting results demonstrating the credential hardening effect of our scheme, we look into the notion that a user’s familiarity to components of a credential set can non-trivially impact error rates. <s> BIB006 </s> A Survey of Keystroke Dynamics Biometrics <s> Subject Demographic. <s> This paper describes a novel technique to strengthen password authentication system by incorporating multiple keystroke dynamic information under a fusion framework. We capitalize four types of latency as keystroke feature and two methods to calculate the similarity scores between the two given latency. A two layer fusion approach is proposed to enhance the overall performance of the system to achieve near 1.401% Equal Error Rate (EER). We also introduce two additional modules to increase the flexibility of the proposed system. These modules aim to accommodate exceptional cases, for instance, when a legitimate user is unable to provide his or her normal typing pattern due to reasons such as hand injury. <s> BIB007 </s> A Survey of Keystroke Dynamics Biometrics <s> Subject Demographic. <s> 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. <s> BIB008
Most experimental subjects involve people around a researcher's institute ranging from undergraduate and postgraduate students  , researchers BIB002 , academicians, and supporting staffs BIB007 BIB008 . Although it may be argued that these populations may not be able to represent the global community, but it is still the primary option as it is the closest readily available resource. Even though several research works has claimed to involve population from broad age distribution (20 to 60) BIB002 BIB006 BIB003 , emphasis should be placed on a more important aspect, such as the typing proficiency of these users. Apart from BIB001 , where the whole population consists of skilled typists, others involved untrained typists who are familiar with the input device BIB005 BIB004 . However, none of the experiments specifically conducted on users that come from entirely low typing proficiency.