Relevant bibliographies by topics / Keystroke dynamic authentication

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  2. Dissertations / Theses
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Academic literature on the topic 'Keystroke dynamic authentication'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Keystroke dynamic authentication"

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Yunanto, Prasti Eko, and Ari Moesriami Barmawi. "Bimodal Keystroke Dynamics-Based Authentication for Mobile Application Using Anagram." Jurnal Ilmu Komputer dan Informasi 15, no. 2 (2022): 81–91. http://dx.doi.org/10.21609/jiki.v15i2.1015.

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Currently, most of the smartphones recognize uses based on static biometrics, such as face and fingerprint. However, those traits were vulnerable against spoofing attack. For overcoming this problem, dynamic biometrics like the keystroke and gaze are introduced since it is more resistant against spoofing attack. This research focuses on keystroke dynamics for strengthening the user recognition system against spoofing attacks. For recognizing a user, the user keystrokes feature used in the login process is compared with keystroke features stored in the keystroke features database. For evaluating the accuracy of the proposed system, words generated based on the Indonesian anagram are used. Furthermore, for conducting the experiment, 34 participants were asked to type a set of words using the smartphone keyboard. Then, each user’s keystroke is recorded. The keystroke dynamic feature consists of latency and digraph which are extracted from the record. According to the experiment result, the error of the proposed method is decreased by 23.075% of EER with FAR and FRR are decreased by 16.381% and 10.41% respectively, compared with Kim’s method. It means that the proposed method is successful increase the biometrics performance by reducing the error rates
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Ugwunna, C.O., O.E. Chukwuogo, O.A. Alabi, M.K. Kareem, T.S. Belonwu, and S.O. Oloyede. "Improving network security using keyboard dynamics: A comparative study." International Research Journal of Science, Technology, Education, and Management 3, no. 4 (2023): 104–21. https://doi.org/10.5281/zenodo.10516267.

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Duplication or imitation of individual keystroke rhymes is very difficult which can make it very efficient to be used for identity authentication. Over time, it is possible that the keystroke style of an individual to be learned by following keystroke information obtained when the person types text. The user’s identity can always be verified by studying the user’s keyboard input styles anytime the user uses the keyboard. The technique suggested in this study uses the keystrokes that users make while typing to verify their identities. To provide an accurate verification of whether a user is authentic or fraudulent, a model that integrates machine learning and dynamic keystroke models—Decision Tree, Random Forest, Support Vector Machine, and K-nearest Neighbors—is compared and utilized. The keystroke dynamics dataset was gathered from Kaggle and consists of 51 subjects' keyboard dynamics data, which was collected over the course of eight sessions and six months. There are 20400 samples in all in the data. This study assessed the effectiveness of machine learning algorithms with a focus on the keystroke dynamic authentication system. Python is used for the development work, while Jupyter notebook is used as the IDE. The performance of the models for different variables is assessed using the following metrics: accuracy, error equal rate, parameter performance, threshold, training time, and testing time. According to the results, the accuracy of the Random Forest, Support Vector Machine, KNN, and Decision Tree algorithms are, respectively, 98, 97.55, 97.28, and 94.26%. Based on the comparing results, Random Forest outperforms the other models, suggesting that Random Forest can be used as the system model for Keystroke Dynamic authentication.
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Ho, Jiacang, and Dae-Ki Kang. "Sequence Alignment with Dynamic Divisor Generation for Keystroke Dynamics Based User Authentication." Journal of Sensors 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/935986.

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Keystroke dynamics based authentication is one of the prevention mechanisms used to protect one’s account from criminals’ illegal access. In this authentication mechanism, keystroke dynamics are used to capture patterns in a user typing behavior. Sequence alignment is shown to be one of effective algorithms for keystroke dynamics based authentication, by comparing the sequences of keystroke data to detect imposter’s anomalous sequences. In previous research, static divisor has been used for sequence generation from the keystroke data, which is a number used to divide a time difference of keystroke data into an equal-length subinterval. After the division, the subintervals are mapped to alphabet letters to form sequences. One major drawback of this static divisor is that the amount of data for this subinterval generation is often insufficient, which leads to premature termination of subinterval generation and consequently causes inaccurate sequence alignment. To alleviate this problem, we introduce sequence alignment of dynamic divisor (SADD) in this paper. In SADD, we use mean of Horner’s rule technique to generate dynamic divisors and apply them to produce the subintervals with different length. The comparative experimental results with SADD and other existing algorithms indicate that SADD is usually comparable to and often outperforms other existing algorithms.
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Sae-Bae, Napa, and Nasir Memon. "Distinguishability of keystroke dynamic template." PLOS ONE 17, no. 1 (2022): e0261291. http://dx.doi.org/10.1371/journal.pone.0261291.

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When keystroke dynamics are used for authentication, users tend to get different levels of security due to differences in the quality of their templates. This paper addresses this issue by proposing a metric to quantify the quality of keystroke dynamic templates. That is, in behavioral biometric verification, the user’s templates are generally constructed using multiple enrolled samples to capture intra-user variation. This variation is then used to normalize the distance between a set of enrolled samples and a test sample. Then a normalized distance is compared against a predefined threshold value to derive a verification decision. As a result, the coverage area for accepted samples in the original space of vector representation is discrete. Therefore, users with the higher intra-user variation suffer higher false acceptance rates (FAR). This paper proposes a metric that can be used to reflect the verification performance of individual keystroke dynamic templates in terms of FAR. Specifically, the metric is derived from statistical information of user-specific feature variations, and it has a non-decreasing property when a new feature is added to a template. The experiments are performed based on two public keystroke dynamic datasets comprising of two main types of keystroke dynamics: constrained-text and free-text, namely the CMU keystroke dynamics dataset and the Web-Based Benchmark for keystroke dynamics dataset. Experimental results based on multiple classifiers demonstrate that the proposed metric can be a good indicator of the template’s false acceptance rate. Thus, it can be used to enhance the security of the user authentication system based on keystroke dynamics.
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Didih, Rizki Chandranegara, Wibowo Hardianto, and Eko Minarno Agus. "Combined scaled manhattan distance and mean of horner's rules for keystroke dynamic authentication." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 2 (2020): 770–75. https://doi.org/10.12928/TELKOMNIKA.v18i2.14815.

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Account security was determined by how well the security techniques applied by the system were used. There had been many security methods that guaranteed the security of their accounts, one of which was Keystroke Dynamic Authentication. Keystroke Dynamic Authentication was an authentication technique that utilized the typing habits of a person as a security measurement tool for the user account. From several research, the average use in the Keystroke Dynamic Authentication classification is not suitable, because a user's typing speed will change over time, maybe faster or slower depending on certain conditions. So, in this research, we proposed a combination of the Scaled Manhattan Distance method and the Mean of Horner's Rules as a classification method between the user and attacker against the Keystroke Dynamic Authentication. The reason for using Mean of Horner’s Rules can adapt to changes in values over time and based on the results can improve the accuracy of the previous method.
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Siti, Rahayu Selamat, Teck Guan Teh, and Yusof Robiah. "ENHANCED AUTHENTICATION FOR WEB-BASED SECURITY USING KEYSTROKE DYNAMICS." International Journal of Network Security & Its Applications (IJNSA) 12, no. 4 (2020): 01–16. https://doi.org/10.5281/zenodo.3975709.

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Current password authentication system was proven not secure enough to protect the information from intruders. However, various research has been done and the results show the value of FRR still low and the value of FAR still high. Thus, one of the methods suggests, is enhancing the current system using keystroke dynamics. Keystroke dynamics is a type of biometric authentication that does not require any special hardware, easy to use as the same routine as normal password authentication. Therefore, this research proposed an authentication system using keystroke dynamics to prevent the system from intruders. A system is developed that consist of two parts which are enrolment and verification. Then, a prototype is developed for testing process that consists of 3 main modules, namely Enrolment, Client/Server Connection and, Verification and Retraining. Based on the testing, the system proved that the keystroke dynamic authentication system was able to implement in client/server environment and shows the value of EER is low that indicates it provide a better system authentication. In future, the system can be improved by enhancing the security, performance, and user interface.
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Panfilova, I. E., and N. E. Karpova. "Investigate the impact of user’s state on the quality of authentication by keystroke dynamic." Journal of Physics: Conference Series 2182, no. 1 (2022): 012097. http://dx.doi.org/10.1088/1742-6596/2182/1/012097.

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Abstract One of the most important issues in the field of information security has been and remains the issue a reliable user’s authentication. A special place among the possible authentication methods today is occupied by behavioural biometrics, which have a high degree of reliability. A keystroke dynamic, as a type of behavioural biometrics, is also capable of providing a high level of protection of information systems in the case of correctly selected characteristics. This article shows that external factors, including the psychophysiological state of a person, can also influence the authentication process by keystroke dynamics. In the paper, the state of the user was assessed in the process of collecting a sample of keystroke dynamics and a conclusion was made about the presence of such an influence.
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Mao, Rui, Xiaoyu Wang, and Heming Ji. "ACBM: attention-based CNN and Bi-LSTM model for continuous identity authentication." Journal of Physics: Conference Series 2352, no. 1 (2022): 012005. http://dx.doi.org/10.1088/1742-6596/2352/1/012005.

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With the evolution of network attack methods, implicit continuous identity authentication technology has attracted more and more attention. Among them, keystroke dynamics is widely used because it does not need the assistance of devices other than keyboards. In this paper, we propose a keystroke dynamic identity authentication model based on deep learning. This model combines convolutional neural network (CNN), bi-directional Long Short-Term Memory (BI-LSTM), and the attention mechanism. Unlike most existing models that only use keystroke time as the feature vector, this model uses keystroke content and keystroke time as the feature vector. First, CNN is used to process feature vectors. Then the normalized vector is input into the bi-LSTM network for training. The model in this paper is tested using Buffalo open data set. The results show that FRR (False Reject Rate), FAR (False Accept Rate), and EER(Equal Error Rate) are 3.09%, 3.03%, and 4.23%, respectively. The validity and accuracy of the model in continuous identity authentication are proved.
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Shradha Pandule, Akshada Ringe, Jaid Sayyed, and Prof. S. C. Puranik. "Keystroke Tracking-Robust System with Dual-Keypad Security." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2098–102. https://doi.org/10.47392/irjaeh.2025.0306.

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The development of an advanced authentication mechanism designed to mitigate the increasing risk of keylogging attacks, a prevalent cyber threat that captures keystrokes to steal sensitive information. Traditional authentication methods relying on keyboard input are particularly vulnerable to such attacks. To address this, a novel security framework is introduced, combining two innovative components: a dual-keypad input system and a visual authentication protocol. The dual-keypad system employs two distinct input interfaces—a physical keypad and a virtual keypad—each handling separate aspects of the authentication process. This division significantly complicates the ability of keyloggers to capture complete authentication sequences, thereby enhancing security. Complementing this, the visual authentication component incorporates a dynamic, graphical verification process. Users interact with visual elements, such as images or patterns displayed on a screen, which are inherently resistant to keylogging. This approach not only strengthens security but also ensures a user-friendly experience. Together, these systems form a multi-layered defense strategy. The dual-keypad mechanism minimizes the risk of keystroke compromise, while the visual authentication ensures that even if keystrokes are intercepted, the authentication process remains secure. This research aims to deliver a robust, secure, and intuitive authentication solution that effectively counters keylogging and other cyber threats, offering a reliable method for safeguarding sensitive information across various applications.
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Fouad, Khaled Mohammed, Basma Mohammed Hassan, and Mahmoud F. Hassan. "User Authentication based on Dynamic Keystroke Recognition." International Journal of Ambient Computing and Intelligence 7, no. 2 (2016): 1–32. http://dx.doi.org/10.4018/ijaci.2016070101.

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Biometric identification is a very good candidate technology, which can facilitate a trusted user authentication with minimum constraints on the security of the access point. However, most of the biometric identification techniques require special hardware, thus complicate the access point and make it costly. Keystroke recognition is a biometric identification technique which relies on the user behavior while typing on the keyboard. It is a more secure and does not need any additional hardware to the access point. This paper presents a developed behavioral biometric authentication method which enables to identify the user based on his Keystroke Static Authentication (KSA) and describes an authentication system that explains the ability of keystroke technique to authenticate the user based on his template profile saved in the database. Also, an algorithm based on dynamic keystroke analysis has been presented, synthesized, simulated and implemented on Field Programmable Gate Array (FPGA). The proposed algorithm is tested on 25 individuals, achieving a False Rejection Rate (FRR) about 4% and a False Acceptance Rate (FAR) about 0%. This performance is reached using the same sampling text for all the individuals. In this paper, two methods are used to implement the proposed approach: method one (H/W based Sorter) and method two (S/W based Sorter) are achieved execution time about 50.653 ns and 9.650 ns, respectively. Method two achieved a lower execution time; the time in which the proposed algorithm is executed on FPGA board, compared to some published results. As the second method achieved a small execution time and area utilization so it is the preferred method to be implemented on FPGA.
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Dissertations / Theses on the topic "Keystroke dynamic authentication"

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Kulich, Martin. "Dynamic Template Adjustment in Continuous Keystroke Dynamics." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234927.

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Dynamika úhozů kláves je jednou z behaviorálních biometrických charakteristik, kterou je možné použít pro průběžnou autentizaci uživatelů. Vzhledem k tomu, že styl psaní na klávesnici se v čase mění, je potřeba rovněž upravovat biometrickou šablonu. Tímto problémem se dosud, alespoň pokud je autorovi známo, žádná studie nezabývala. Tato diplomová práce se pokouší tuto mezeru zaplnit. S pomocí dat o časování úhozů od 22 dobrovolníků bylo otestováno několik technik klasifikace, zda je možné je upravit na online klasifikátory, zdokonalující se bez učitele. Výrazné zlepšení v rozpoznání útočníka bylo zaznamenáno u jednotřídového statistického klasifikátoru založeného na normované Euklidovské vzdálenosti, v průměru o 23,7 % proti původní verzi bez adaptace, zlepšení však bylo pozorováno u všech testovacích sad. Změna míry rozpoznání správného uživatele se oproti tomu různila, avšak stále zůstávala na přijatelných hodnotách.
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Mattsson, Rebecka. "Keystroke dynamics for student authentication in online examinations." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-79454.

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Biometrics are distinctive for each person, and can not be given away or hacked like a password. Keystroke dynamics is a behavioral biometric characteristic that can be used as a complementary authentication step [1]. In online examinations it is difficult to make sure that each student write their own work. Keystroke dynamics from these examinations could be used to detect attempted cheating. To detect cheating attempts, a Gaussian Mixture Models with Universal Background Model (GMM-UBM) was implemented, and tested on benchmark data set recorded from online examinations written in free text. The use of a Universal Background Model (UBM) allows students to be enrolled using a limited amount of data, making the sug- gested approach suitable for the use case. The use of a GMM-UBM resulted in an Equal Error Rate (ERR) of 5.4% and an accuracy of 94.5%.
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Alsolami, Eesa. "An examination of keystroke dynamics for continuous user authentication." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/54730/1/Eesa_Alsolami_Thesis.pdf.

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Most current computer systems authorise the user at the start of a session and do not detect whether the current user is still the initial authorised user, a substitute user, or an intruder pretending to be a valid user. Therefore, a system that continuously checks the identity of the user throughout the session is necessary without being intrusive to end-user and/or effectively doing this. Such a system is called a continuous authentication system (CAS). Researchers have applied several approaches for CAS and most of these techniques are based on biometrics. These continuous biometric authentication systems (CBAS) are supplied by user traits and characteristics. One of the main types of biometric is keystroke dynamics which has been widely tried and accepted for providing continuous user authentication. Keystroke dynamics is appealing for many reasons. First, it is less obtrusive, since users will be typing on the computer keyboard anyway. Second, it does not require extra hardware. Finally, keystroke dynamics will be available after the authentication step at the start of the computer session. Currently, there is insufficient research in the CBAS with keystroke dynamics field. To date, most of the existing schemes ignore the continuous authentication scenarios which might affect their practicality in different real world applications. Also, the contemporary CBAS with keystroke dynamics approaches use characters sequences as features that are representative of user typing behavior but their selected features criteria do not guarantee features with strong statistical significance which may cause less accurate statistical user-representation. Furthermore, their selected features do not inherently incorporate user typing behavior. Finally, the existing CBAS that are based on keystroke dynamics are typically dependent on pre-defined user-typing models for continuous authentication. This dependency restricts the systems to authenticate only known users whose typing samples are modelled. This research addresses the previous limitations associated with the existing CBAS schemes by developing a generic model to better identify and understand the characteristics and requirements of each type of CBAS and continuous authentication scenario. Also, the research proposes four statistical-based feature selection techniques that have highest statistical significance and encompasses different user typing behaviors which represent user typing patterns effectively. Finally, the research proposes the user-independent threshold approach that is able to authenticate a user accurately without needing any predefined user typing model a-priori. Also, we enhance the technique to detect the impostor or intruder who may take over during the entire computer session.
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Ozdemir, Musa Kazim. "A Framework For Authentication Of Medical Reports Based On Keystroke Dynamics." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612081/index.pdf.

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Privacy of personal health records is of ultimate importance. Unfortunately, it is easy to obtain illegal access to electronic health records under insufficient security precautions. Access control based on token or username/password is not adequate for applications in health domain which require heightened security. Currently, electronic signature mechanisms are being employed as a strong alternative to classic methods. In addition, biometrics provide more precise results in comparison to electronic signature methods. However, applicability of biometrics in this field has been prohibited by factors such as the need for special hardware, increased implementation costs, and invasiveness of the biometry sensors (eg. iris topology, fingerprint). Behavioral biometrics such as speech, and keystroke dynamics are easier to implement, and do not suffer from the disadvantages mentioned for the static biometrics. Especially, using keystroke dynamics for user authentication is more advantageous than other advanced biometrics because the implementation is inexpensive and continuous identity control is plausible. The aim of this study is to show the feasibility of merging a biometry-based advanced identity verification method together with an initial access control procedure such as password check. In this study, we provide an authentication framework based on measuring similarity of the typing characteristics of medical reporters, while they are typing medical reports. We have made a prototype of the system and provided classification of keystroke timings for each operator. We have generated a testbed and measured similarity of typing patterns of 5 medical reporters upon typing 4 different kinds of medical reports. Our system performs with hundred percent accuracy in identifying the authorized operators from the reports they type. In current practice, electronic signatures are indispensable for health information systems, but our study shows that keystroke dynamics can easily be included in this chain for increased security.
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Dorca, Josa Aleix. "Identifying users using Keystroke Dynamics and contextual information." Doctoral thesis, Universitat d'Andorra, 2018. http://hdl.handle.net/10803/461468.

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Biometric identification systems based on Keystroke Dynamics have been around for almost forty years now. There has always been a lot of interest in identifying individuals using their physiological or behavioral traits. Keystroke Dynamics focuses on the particular way a person types on a keyboard. The objective of the proposed research is to determine how well the identity of users can be established when using this biometric trait and when contextual information is also taken into account. The proposed research focuses on free text. Users were never told what to type, how or when. This particular field of Keystroke Dynamics has not been as thoroughly studied as the fixed text alternative where a plethora of methods have been tried. The proposed methods focus on the hypothesis that the position of a particular letter, or combination of letters, in a word is of high importance. Other studies have not taken into account if these letter combinations had occurred at the beginning, the middle, or the end of a word. A template of the user will be built using the context of the written words and the latency between successive keystrokes. Other features, like word length, minimum number of needed words to consider a session valid, frequency of words, model building parameters, as well as age group and gender have also been studied to determine those that better help ascertain the identity of an individual. The results of the proposed research should help determine if using Keystroke Dynamics and the proposed methodology are enough to identify users from the content they type with a good enough level of certainty. From this moment, it could be used as a method to ensure that a user is not supplanted, in authentication schemes, or even to help determine the authorship of different parts of a document written by more than one user.<br>Els sistemes d’identificació biomètrica basades en la cadència de tecleig fa gairebé quaranta anys que s’estudien. Hi ha hagut molt interès en identificar les persones a partir de les seves característiques fisiològiques o de comportament. La cadència de tecleig és la manera en la que una persona escriu en un teclat. L’objectiu de la recerca proposada és determinar com de bé es pot arribar a identificar un individu mitjançant aquesta característica biomètrica i quan també es prenen en consideració dades contextuals. Aquesta recerca es basa en text lliure. Als usuaris mai se’ls va dir què, quan o com havien d’escriure. Aquest camp de la cadència de tecleig no ha estat tan estudiat com l’alternativa de text fix on un gran ventall de mètodes s’han provat. Els mètodes d’identificació proposats es basen en la hipòtesi que la posició d’una lletra, o combinació de lletres teclejades, en una paraula és de gran importància. Altres estudis no prenen en consideració aquesta informació, és a dir, si la combinació de lletres s’ha produït al principi, al mig o al final de la paraula. Es crearà una empremta de l’usuari tenint en compte el context de les lletres en les paraules escrites i les latències entre pulsacions successives. Altres característiques com la mida de les paraules, el nombre mínim de paraules necessari per considerar una sessió vàlida, la freqüència de mots, els paràmetres de construcció dels models, així com el grup d’edat i el gènere també s’han estudiat per determinar quines són les que millor ajuden a identificar un individu. Els resultats de la recerca proposada haurien de permetre determinar si l’ús de la cadència de tecleig i els mètodes proposats són suficients per identificar els usuaris a partir del contingut que generen, sempre amb un cert marge d’error. En cas afirmatiu es podria introduir la tècnica proposada com un mètode més per assegurar que un usuari no és suplantat, en sistemes d’autenticació, o fins i tot per ajudar a determinar l’autoria de diferents parts d’un document que ha estat escrit per més d’un usuari.
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Alsultan, Arwa Fahad A. "Free-text keystroke dynamics authentication with a reduced need for training and language independency." Thesis, University of Reading, 2017. http://centaur.reading.ac.uk/69412/.

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This research aims to overcome the drawback of the large amount of training data required for free-text keystroke dynamics authentication. A new key-pairing method, which is based on the keyboard’s key-layout, has been suggested to achieve that. The method extracts several timing features from specific key-pairs. The level of similarity between a user’s profile data and his or her test data is then used to decide whether the test data was provided by the genuine user. The key-pairing technique was developed to use the smallest amount of training data in the best way possible which reduces the requirement for typing long text in the training stage. In addition, non-conventional features were also defined and extracted from the input stream typed by the user in order to understand more of the users typing behaviours. This helps the system to assemble a better idea about the user’s identity from the smallest amount of training data. Non-conventional features compute the average of users performing certain actions when typing a whole piece of text. Results were obtained from the tests conducted on each of the key-pair timing features and the non-conventional features, separately. An FAR of 0.013, 0.0104 and an FRR of 0.384, 0.25 were produced by the timing features and non-conventional features, respectively. Moreover, the fusion of these two feature sets was utilized to enhance the error rates. The feature-level fusion thrived to reduce the error rates to an FAR of 0.00896 and an FRR of 0.215 whilst decision-level fusion succeeded in achieving zero FAR and FRR. In addition, keystroke dynamics research suffers from the fact that almost all text included in the studies is typed in English. Nevertheless, the key-pairing method has the advantage of being language-independent. This allows for it to be applied on text typed in other languages. In this research, the key-pairing method was applied to text in Arabic. The results produced from the test conducted on Arabic text were similar to those produced from English text. This proves the applicability of the key-pairing method on a language other than English even if that language has a completely different alphabet and characteristics. Moreover, experimenting with texts in English and Arabic produced results showing a direct relation between the users’ familiarity with the language and the performance of the authentication system.
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Gurary, Jonathan Gurary. "Improving the Security of Mobile Devices Through Multi-Dimensional and Analog Authentication." Cleveland State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=csu1521564381685222.

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Липовий, Т. В., та T. V. Lypovyi. "Комп’ютерна система біометричної аутентифікації особи за клавіатурним почерком". Thesis, Тернопільський національний технічний університет імені Івана Пулюя, 2015. http://elartu.tntu.edu.ua/handle/lib/20793.

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У роботі розглянуті питання дослідження методів біометричної аутентифікації особи за клавіатурним почерком. Проведений аналіз існуючих методів та математичних моделей опрацювання даних в комп’ютерній системі біометричної аутентифікації за клавіатурним почерком, вперше запропоновано як інформативну ознаку в подібних системах використовувати значення коефіцієнтів кореляції, що дозволило виявити взаємозв’язок між набраними символами в парольній фразі.<br>The purpose of the research is to explore the keystroke dynamics biometrical authentication methods for the possibility to increase the reliability of the computer systems safety Research object is keystroke dynamics biometric authentication process. Research subjects are keystroke dynamics biometric authentication methods and algorithms in computer systems, which provide the best system’s safety. There is a review of keystroke dynamics biometric authentication researches in master’s work. There is an analysis of existing mathematical data processing methods and models in the research, which have place in the keystroke dynamics biometric authentication computer systems, proposed to use correlation coefficients as informational feature for the first time.
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Hsin, Tsung-Chin, and 辛宗秦. "Design and Implementation of User Authentication Based on Keystroke Dynamic." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/gm6m7t.

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碩士<br>國立中山大學<br>資訊工程學系研究所<br>96<br>In the traditional login systems, we use the username and the password to identify the legalities of users. It is a simple and convenient way to identify, but passwords could be stolen or copied by someone who tries to invade the system illegally. Adding one protective mechanism to identify users, the way of biometrics are brought out, such as keystroke dynamics, fingerprints, DNA, retinas and so on that are unique characteristics of each individuals, it could be more effective in preventing trespassing. This thesis uses keystroke biometrics as research aspects of user authentication. The advantages of this system are low-cost and high security to identify users using keyboard to calculate the time of keystrokes. In this thesis, we use statistical way to examine the researches and experiments. Chosen length of the username and password are greater than or equal to 9 characters, and learning sample sizes are 20 and adapting the sample adaptation mechanism, the results show that we achieved by False Acceptance Rate of 0.85%, False Rejection Rate of 1.51% and Average False Rate of 1.18%; all reach the high levels of safeties.
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Alshanketi, Faisal. "Enhanced usability, resilience, and accuracy in mobile keystroke dynamic biometric authentication." Thesis, 2018. https://dspace.library.uvic.ca//handle/1828/10093.

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With the progress achieved to this date in mobile computing technologies, mobile devices are increasingly being used to store sensitive data and perform security-critical transactions and services. However, the protection available on these devices is still lagging behind. The primary and often only protection mechanism in these devices is authentication using a password or a PIN. Passwords are notoriously known to be a weak authentication mechanism, no matter how complex the underlying format is. Mobile authentication can be strengthened by extracting and analyzing keystroke dynamic biometric from supplied passwords. In this thesis, I identified gaps in the literature, and investigated new models and mechanisms to improve accuracy, usability and resilience against statistical forgeries for mobile keystroke dynamic biometric authentication. Accuracy is investigated through cost sensitive learning and sampling, and by comparing the strength of different classifiers. Usability is improved by introducing a new approach for typo handling in the authentication model. Resilience against statistical attacks is achieved by introducing a new multimodal approach combining fixed and variable keystroke dynamic biometric passwords, in which two different fusion models are studied. Experimental evaluation using several datasets, some publicly available and others collected locally, yielded encouraging performance results in terms of accuracy, usability, and resistance against statistical attacks.<br>Graduate<br>2019-09-25
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Books on the topic "Keystroke dynamic authentication"

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Zhong, Yu, and Yunbin Deng, eds. Recent Advances in User Authentication Using Keystroke Dynamics Biometrics. Science Gate Publishing P.C., 2015. http://dx.doi.org/10.15579/gcsr.vol2.

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Book chapters on the topic "Keystroke dynamic authentication"

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Alshanketi, Faisal, Issa Traoré, Awos Kanan, and Ahmed Awad. "Adaptive Mobile Keystroke Dynamic Authentication Using Ensemble Classification Methods." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03712-3_4.

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Choe, YeongGeun, and Soon-Ja Kim. "Secure Password Authentication for Keystroke Dynamics." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553939_46.

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Raul, Nataasha, Royston D’mello, and Mandar Bhalerao. "Keystroke Dynamics Authentication Using Small Datasets." In Communications in Computer and Information Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7561-3_7.

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Shanmugapriya, Venkateswaran, and Ganapathi Padmavathi. "Keystroke Dynamics Authentication Using Neural Network Approaches." In Information and Communication Technologies. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15766-0_121.

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Kolakowska, Agata. "User Authentication Based on Keystroke Dynamics Analysis." In Computer Recognition Systems 4. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20320-6_68.

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Li, Jianwei, Han-Chih Chang, and Mark Stamp. "Free-Text Keystroke Dynamics for User Authentication." In Advances in Information Security. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97087-1_15.

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Dutta, Sandip, Soumen Roy, and Utpal Roy. "Advanced Keystroke Dynamics for Secure Smartphone Authentication." In Learning and Analytics in Intelligent Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82706-8_15.

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Abhimanyu and Tripti Rathee. "Keystroke Dynamics: Authenticating Users by Typing Pattern." In Communications in Computer and Information Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3433-6_46.

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de Lima e Silva Filho, Sergio Roberto, and Mauro Roisenberg. "Continuous Authentication by Keystroke Dynamics Using Committee Machines." In Intelligence and Security Informatics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760146_90.

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Roštár, Roman, and Jaroslav Olejár. "Keystroke Dynamics Based User Authentication Using Neural Networks." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 1995. http://dx.doi.org/10.1007/978-3-7091-7535-4_52.

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Conference papers on the topic "Keystroke dynamic authentication"

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Wang, Xiaofei, and Daqing Hou. "User-Specific Feature Selection in Keystroke Dynamics Authentication." In 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC). IEEE, 2025. https://doi.org/10.1109/ccnc54725.2025.10975900.

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Kek, Xin-Jin, Yu-Beng Leau, and Soo Fun Tan. "User Authentication with Keystroke Dynamics: Performance Evaluation in Neural Network." In 2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). IEEE, 2024. http://dx.doi.org/10.1109/iicaiet62352.2024.10730363.

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Kamra, Anurag, Sanya Khurana, and Anurag Goel. "Keystroke Dynamics Based User Authentication Focusing on Fixed Text Approaches." In 2025 3rd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT). IEEE, 2025. https://doi.org/10.1109/dicct64131.2025.10986481.

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Gupta, Taduvai Satvik, Konduru Praveen Karthik, Sri Sai Suhas Sanisetty, and Sagar Basavaraju. "An Ensemble Model for User Authentication Leveraging Keystroke Dynamics and Facial Recognition." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859646.

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Pramana, Made Indra Wira, Suhardi, Novianto Budi Kurniawan, and Jaka Sembiring. "Keystroke dynamics for authentication using dynamic time warping." In 2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2017. http://dx.doi.org/10.1109/jcsse.2017.8025915.

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Ho, Jiacang, and Dae-Ki Kang. "Sequence alignment of dynamic intervals for keystroke dynamics based user authentication." In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2014. http://dx.doi.org/10.1109/scis-isis.2014.7044658.

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Bhattasali, Tapalina, Piotr Panasiuk, Khalid Saeed, Nabendu Chaki, and Rituparna Chaki. "Modular logic of authentication using dynamic keystroke pattern analysis." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015). Author(s), 2016. http://dx.doi.org/10.1063/1.4951959.

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Alshanketi, Faisal, Issa Traore, and Ahmed Awad Ahmed. "Improving Performance and Usability in Mobile Keystroke Dynamic Biometric Authentication." In 2016 IEEE Security and Privacy Workshops (SPW). IEEE, 2016. http://dx.doi.org/10.1109/spw.2016.12.

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Jeanjaitrong, Nattapong, and Pattarasinee Bhattarakosol. "Feasibility study on authentication based keystroke dynamic over touch-screen devices." In 2013 13th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2013. http://dx.doi.org/10.1109/iscit.2013.6645856.

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Raul, Nataasha, Radha Shankarmani, and Padmaja Joshi. "Static Keystroke Dynamic Authentication (SKDA) Model to Authenticate User during Password Change." In 2023 3rd International Conference on Smart Data Intelligence (ICSMDI). IEEE, 2023. http://dx.doi.org/10.1109/icsmdi57622.2023.00054.

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