A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> This paper presents techniques to verify the identity of computer users using the keystroke dynamics of computer user's login string as characteristic patterns using pattern recognition and neural network techniques. This work is a continuation of our previous work where only interkey times were used as features for identifying computer users. In this work we used the key hold times for classification and then compared the performance with the former interkey time-based technique. Then we use the combined interkey and hold times for the identification process. We applied several neural network and pattern recognition algorithms for verifying computer users as they type their password phrases. It was found that hold times are more effective than interkey times and the best identification performance was achieved by using both time measurements. An identification accuracy of 100% was achieved when the combined hold and intekey time-based approach were considered as features using the fuzzy ARTMAP, radial basis function networks (RBFN), and learning vector quantization (LVQ) neural network paradigms. Other neural network and classical pattern algorithms such as backpropagation with a sigmoid transfer function (BP, Sigm), hybrid sum-of-products (HSOP), sum-of-products (SOP), potential function and Bayes' rule algorithms gave moderate performance. <s> BIB001 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> 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. <s> BIB002 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <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> BIB003 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> The progress in the field of human-computer interaction with hand held electronic devices, such as, personal digital assistants (PDAs) and mobile phones searches for new interaction techniques. Proximity sensing extends the concept of computer-human interaction beyond actual physical contact with a device. In this paper, a virtual keyboard implementation is presented and keystroke recognition experiments with the keyboard utilizing proximity measurements are described. An infrared (IR) transceiver array is used for detecting the proximity of a finger. Keystroke recognition accuracy is examined with k-nearest neighbor (k-NN) classifier while a multilayer perceptron (MLP) classifier is designed for online implementation. Experiments and results of keystroke classification are presented for both classifiers. The recognition accuracy, which is between 78% and 99% for k-NN classifier and between 69% and 96% for MLP classifier, depends mainly on the location of a specific key on the keyboard area. <s> BIB004 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> 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. <s> BIB005 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> This work describes a clustering-based system to enhance user authentication by applying fuzzy techniques to biometric data in order to deter password sharing. Fuzzy c-means is used to train personal, per-keyboard profiles based on the keystroke dynamics of users when entering passwords on a keyboard. These profiles use DES encryption taking the actual passwords as key and are read at logon time by the access control mechanism in order to further validate the identity of the user. Fuzzy values obtained from membership functions applied to the input (i.e., keystroke latencies) are compared against profile values, and a match, within a certain precision threshold /spl gamma/, will grant access to the user. With this technique, even when user A shares password P/sub A/ with user B, B will still be denied access unless he is capable of mimicking the keystroke dynamics of A. We describe the motivation, design, and implementation of a prototype whose results indicate the accuracy level and feasibility of the approach. <s> BIB006 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> To detect the novel impostors whose data patterns have never been learned previously in keystroke dynamics, two solutions are proposed in this paper. Unlike most other research in keystroke dynamics, this paper surveys the performance tradeoff and time consumption, which are valuable for practical implementation, of the solutions. Besides, it is our intention to attempt verifying computer users’ identities based on pure numeric password which is more difficult than verification of any other kinds of passwords. <s> BIB007 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> We propose a Monte Carlo approach to attain sufficient training data, a splitting method to improve effectiveness, and a system composed of parallel decision trees (DTs) to authenticate users based on keystroke patterns. For each user, approximately 19 times as much simulated data was generated to complement the 387 vectors of raw data. The training set, including raw and simulated data, is split into four subsets. For each subset, wavelet transforms are performed to obtain a total of eight training subsets for each user. Eight DTs are thus trained using the eight subsets. A parallel DT is constructed for each user, which contains all eight DTs with a criterion for its output that it authenticates the user if at least three DTs do so; otherwise it rejects the user. Training and testing data were collected from 43 users who typed the exact same string of length 37 nine consecutive times to provide data for training purposes. The users typed the same string at various times over a period from November through December 2002 to provide test data. The average false reject rate was 9.62% and the average false accept rate was 0.88%. <s> BIB008 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> Software based biometrics, utilising keystroke dynamics has been proposed as a cost effective means of enhancing computer access security. Keystroke dynamics has been successfully employed as a means of identifying legitimate/illegitimate login attempts based on the typing style of the login entry. In this paper, we collected keystroke dynamics data in the form of digraphs from a series of users entering a specific login ID. We wished to determine if there were any particular patterns in the typing styles that would indicate whether a login attempt was legitimate or not using rough sets. Our analysis produced a sensitivity of 96%, specificity of 93% and an overall accuracy of 95%. The results of this study indicate that typing speed and the first few and the last few characters of the login ID were the most important indicators of whether the login attempt was legitimate or not <s> BIB009 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> 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. <s> BIB010 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> In keystroke dynamics-based authentication, novelty detection methods are used since only the valid user's patterns are available when a classifier is first constructed. After a while, however, impostors' keystroke patterns become available from failed login attempts. We propose to employ the retraining framework where a novelty detector is retrained with the impostor patterns to enhance authentication accuracy. In this paper, learning vector quantization for novelty detection and support vector data description are retrained with the impostor patterns. Experimental results show that retraining improves the authentication performance and that learning vector quantization for novelty detection outperforms other widely used novelty detectors. <s> BIB011 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> Biometrics technology has emerged as the desired solution for a range of secure applications where a person's identity must be determined. Biometrics is currently controlling network and physical access in numerous high risk areas, including airport customs and immigration desks, financial malls among others. This paper compares applications of neural networks to the field of hardened password mechanism in a typical workplace environment. <s> BIB012 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> 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 combination of maximum pressure exerted on the keyboard and time latency between keystrokes is used as features to create typing patterns for individual users so as to recognize authentic users and to reject impostors. Support vector machines (SVMs), which is relatively new machine learning, is used as a pattern matching method. 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 is effective for biometric-based security system. <s> BIB013 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> 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. <s> BIB014 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <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> BIB015 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> The present invention provides apparatus for changing the direction of rotation of a rotating shaft without changing the direction of rotation of the motor driving the shaft. The present invention includes a drive gear which causes two driven bevel gears to rotate in opposite directions. A control shaft having cam surfaces selectively forces one of two sets of balls into engagement with one of the driven gears to couple that gear with the driven shaft to rotate the shaft in one direction. Rotation of the driven shaft is reversed by changing the position of the control shaft which uncouples one set of balls from the driven shaft and couples the remaining set of balls to the driven shaft. <s> BIB016 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> 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 combination of maximum pressure exerted on the keyboard and time latency between keystrokes is used as features to create typing patterns for individual users so as to recognize authentic users and to reject impostors. Radial basis function network (RBFN), which is one of the artificial neural network, is used as a pattern matching method. 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 is effective for biometric-based security system. <s> BIB017 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> Keystroke biometric has been widely investigated to enable an ordinary keyboard to be an authenticable input device. Most of the existing studies have analysed keystroke latencies in some frequently-used, short, fixed-length strings, e.g. username and password, to strengthen authentication in general login processes. Meanwhile, a number of studies have been recently proposed to use keystroke latency in free text domain in which a sequence of keystrokes is generally longer than username and password. However, the methods used in both short text and long text are different. General supervised classifiers which are used with a fixed number of attributes cannot be applied to the variable-length free text. In this paper, we thus propose a new method which can employ Backpropagation Neural Network to classify the users using keystroke latency times from non-fixed length text. Our method first clusters the latency between a pair of characters or digraph, and then discovers the group of digraphs which identifies each user. Next, the cluster membership is used as input features of the networks. The results show that our method can recognize unseen users with a higher accuracy than the other methods run in our experiments. <s> BIB018 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> Significant research into the feasibility of keystroke dynamics as a potential biometric authentication method has taken place since the advent of computers. The studies have progressed from examining typing patterns on desktop keyboards using statistical pattern classifiers to mobile keyboards using neural networks as a pattern classifier. The studies do not have a unifying method of comparing results, which limits comparison between the methods presented. Without the ability to compare studies within research areas, future study is limited in its ability to provide important modifications to the work in question. This paper reviews a representative subset of the current research in keystroke dynamics, and provides recommendations on the potential direction of future work in this area. This will provide a set of guidelines that can be followed by researchers intending to do further work in the area of keystroke dynamics. <s> BIB019 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> Keystroke dynamics is the process of identifying individual users on the basis of their typing rhythms, which are in turn derived from the timestamps of key-press and key-release events in the keyboard. Many researchers have explored this domain, with mixed results, but few have examined the relatively impoverished territory of digits only, particularly when restricted to using a single finger - which might come into play on an automated teller machine, a mobile phone, a digital telephone dial, or a digital electronic security keypad at a building entrance. In this work, 28 users typed the same 10-digit number, using only the right-hand index finger. Employing statistical machine-learning techniques (random forest), we achieved an unweighted correct-detection rate of 99.97% with a corresponding false-alarm rate of 1.51%, using practiced 2-of-3 encore typing with outlier handling. This level of accuracy approaches sufficiency for two-factor authentication for passwords or PIN numbers. <s> BIB020 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> The need to secure sensitive data and computer systems from intruders, while allowing ease of access for authenticate user is one of the main problems in computer security. Traditionally, passwords have been the usual method for controlling access to computer systems but this approach has many inherent flaws. Keystroke dynamics is a promising biometric technique to recognize an individual based on an analysis of his/her typing patterns. In the experiment, we measure mean, standard deviation and median values of keystroke features such as latency, duration, digraph and their combinations and compare their performance. Particle swarm optimization (PSO), genetic algorithm (GA) and the proposed ant colony optimization (ACO) are used for feature subset selection. Back propagation neural network (BPNN) is used for classification. ACO gives better performance than PSO and GA with regard to feature reduction rate and classification accuracy. Using digraph as the feature for feature subset selection is novel and show good classification performance. <s> BIB021 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> We describe the use of keystroke-dynamics patterns for authentication and detecting infected hosts, and evaluate its robustness against forgery attacks. Specifically, we present a remote authentication framework called TUBA for monitoring a user's typing patterns. We evaluate the robustness of TUBA through comprehensive experimental evaluation including two series of simulated bots. Support vector machine is used for classification. Our results based on 20 users' keystroke data are reported. Our work shows that keystroke dynamics is robust against synthetic forgery attacks studied, where attacker draws statistical samples from a pool of available keystroke datasets other than the target. TUBA is particularly suitable for detecting extrusion in organizations and protecting the integrity of hosts in collaborative environments, as well as authentication. <s> BIB022 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> We have proposed the integration of behavior biometrics using Supervised Pareto learning SOM to improve the accuracy of authentication. For small systems such as mobile devices, this method may be heavy, because of the memory usage or computational power. In this paper, we propose the application of Concurrent Pareto learning SOM, which uses a small map for each user. The performance of this method is confirmed by authentication experiments using behavior biometrics of keystroke timings and key typing sounds. <s> BIB023 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> Access control systems rely on a variety of methods for authenticating legitimate users and preventing malicious ones from accessing the system. The most commonly used system is a simple username and password approach. This technology has been the de-facto standard for remote authentication applications. A username-password based system assumes that only the genuine users know their own credentials. However, breaching this type of system has become a common occurrence in today's age of social networks and modern computational devices. Once broken, the system will accept every authentication trial using compromised credentials until the breach is detected. In this paper, we explore certain aspects of utilizing keystroke dynamics in username-password based systems. We show that as users get habituated to typing their credentials, there is a significant reduction in the variance of the keystroke patterns. This trend is more pronounced for long and complex passwords as opposed to short dictionary based passwords. We also study the time window necessary to perceive habituation in user typing patterns. Furthermore, we show that habituation plays a key role in classification of genuine login attempts by reducing the equal error rate (EER) over time. Finally, we explore an authentication scheme that employs the security of complex passwords and keystroke dynamics. Access control systems rely on a variety of methods for authenticating legitimate users and preventing malicious ones from accessing the system. The most commonly used system is a simple username and password approach. This technology has been the de-facto standard for remote authentication applications. A username-password based system assumes that only the genuine users know their own credentials. However, breaching this type of system has become a common occurrence in today's age of social networks and modern computational devices. Once broken, the system will accept every authentication trial using compromised credentials until the breach is detected. In this paper, we explore certain aspects of utilizing keystroke dynamics in username-password based systems. We show that as users get habituated to typing their credentials, there is a significant reduction in the variance of the keystroke patterns. This trend is more pronounced for long and complex passwords as opposed to short dictionary based passwords. We also study the time window necessary to perceive habituation in user typing patterns. Furthermore, we show that habituation plays a key role in classification of genuine login attempts by reducing the equal error rate (EER) over time. Finally, we explore an authentication scheme that employs the security of complex passwords and keystroke dynamics. <s> BIB024 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> This paper introduces a new BeiHang (BH) Keystroke Dynamics Database for testing and evaluation of biometric approaches. Different from the existing keystroke dynamics researches which solely rely on laboratory experiments, the developed database is collected from a real commercialized system and thus is more comprehensive and more faithful to human behavior. Moreover, our database comes with ready-to-use benchmark results of three keystroke dynamics methods, Nearest Neighbor classifier, Gaussian Model and One-Class Support Vector Machine. Both the database and benchmark results are open to the public and provide a significant experimental platform for international researchers in the keystroke dynamics area. <s> BIB025 </s> A Survey of Keystroke Dynamics Biometrics <s> Machine Learning. Machine learning is widely used in <s> User authentication via keystroke dynamics remains a challenging problem due to the fact that keystroke dynamics pattern cannot be maintained stable over time. This paper describes a novel keystroke dynamics-based user authentication approach. The proposed approach consists of two stages, a training stage and an authentication stage. In the training stage, a set of orthogonal bases and a common feature vector are periodically generated from keystroke features of a legitimate user@?s several recent successful authentications. In the authentication stage, the current keystroke feature vector is projected onto the set of orthogonal bases, and the distortion of the feature vector between its projection is obtained. User authentication is implemented by comparing the slope correlation degree of the distortion between the common feature vector with a threshold determined periodically using the recent impostor patterns. Theoretical and experimental results show that the proposed method presents high tolerance to instability of user keystroke patterns and yields better performance in terms of false acceptance rate (FAR) and false rejection rate (FRR) compared with some recent methods. <s> BIB026
Neural network is claimed to be capable of producing better result than the statistical methods BIB019 . However, the classifiers require not only genuine keystroke patterns but also intruders' to train the network. It may be impractical to obtain intruders' samples at the initial enrolment stage BIB026  . Furthermore, any addition, removal or update on user profile in the system requires the whole network to be retrained and thus the amount of processing time increases. Database partitioning [20] and retraining during system idle period BIB003 has been suggested as an attempt to resolve this problem. Some widely used neural networks are radial basis function network  BIB017 , learning vector quantization BIB001 BIB011 , multilayer perceptron BIB004 BIB015 BIB012 , and self-organizing map BIB018 BIB023 . Decision tree is a kind of learn by example pattern recognition technique that is suitable for classification problem involving small output class such as genuine or imposter. It is usually less computational intensive as compared to neural network BIB008 . The main concept is to recursively split training data so that the information gain ratio is maximized at each level of node in the tree. This step carries on until each node has only a single class example or information gain is exhausted  . Precaution should be taken to avoid over fitting the tree, which could lead to poor performance as well as high computational complexity. Some tree-based learning methods that are used in the literature were random forest  BIB020 BIB024 and J48  BIB016 . Fuzzy logic uses multivalued logic to model problems with ambiguous data BIB002 . The key idea is to construct the boundaries of decision region based on training data with membership functions and fuzzy rules BIB003 . After the feature space has been identified, the degree of category in which a test template belongs to can be determined based on the computation of membership values. The instances of using fuzzy logic in keystroke dynamics authentication are BIB002  BIB006 . Evolutionary computing has also been explored by researchers in hope to improve accuracy performance. Genetic algorithm BIB010 BIB009 , particle swan optimization  , and ant colony optimization BIB021 are the techniques that have been applied to select the most optimized keystroke feature for classification, thereby increasing classification accuracy. Another renown classifier adopted by many studies BIB013 BIB025 BIB007 , which distinguishes imposter patterns by creating a margin that separates normal patterns from imposters' is called Support vector machine (SVM). This method generates the smallest possible region that encircles the majority of feature data related to a particular class. SVM maps the input vector into a high-dimensional feature space via the kernel function (e.g. linear, polynomial, sigmoid, or radial basis function) BIB022 . The algorithm will then search for a function that encapsulates the majority of patterns contained in the input vector and vector outside this region. As a result, the separating function is able to create more complex boundaries and to better determine which side of feature space a new pattern belongs. SVM is claimed to have a competitive performance as compared to neural network and yet less computational intense BIB005 ; however, the performance is questionable when the feature set is too large BIB014 .