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1

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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5

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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8

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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Lin, Jyun-Hao, and 林俊豪. "Keystroke Dynamic PIN-based and Graphical-based Authentication System - Implemented in Android Compatible Phone." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/67258930883548991161.

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碩士<br>國立彰化師範大學<br>數位學習研究所<br>99<br>Since the touch screen handheld mobile devices have become widely used, people are able to conveniently access various data and information anywhere. Most user authentication methods for these mobile devices are PIN-based (Person Identification Number) authentication. However, PINs consist of 4 to 8 numbers and then it suffers from guessing and shoulder surfing attacks easily. Many studies have employed the KDA (Keystroke Dynamics-based Authentication) system, which verifies PIN correctness and also verifies the corresponding individual keystroke features. It provides additional security verification for PIN-based authentication. Unfortunately, unlike the text-based password KDA systems in QWERTY keyboards, different keypad sizes or layouts of mobile devices affect the PIN-based KDA system utility. In this study, the accuracy of authenticating the keystroke features are not affected by inconsistent keypads since the PINs is entered via the identical size of human-computer interface. Moreover, except for the original keystroke time features in the KDA systems, two novel keystroke features are found in touch screen handheld mobile devices, which are called "pressure" features and "size" features. This study uses these features to improve the keystroke data quality of PIN-based KDA system. On the other hand, we propose a new graphical-based password KDA system for touch screen handheld mobile devices. The proposed system uses graphical password to enlarge the password space size and uses the pressure features to improve the keystroke data quality. A user also enter his/her graphical password via the identical size of human-computer interface. Under this situation, the graphical-based password KDA system is able to promote the PIN-based KDA system utility in touch screen handheld mobile devices.
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Oliveira, Tiago Costa. "Improving web authentication with keystroke dynamics." Master's thesis, 2014. http://hdl.handle.net/1822/36767.

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Dissertação de mestrado em Engenharia Informática<br>Authentication is frequently referred as the most critical part of a computer system security. Users commonly identify themselves using a combination of username and password, but sometimes this is not enough. Concerning web-based services, attacks like phishing or social engineering can easily result in identity theft. In addition, the widespread use of single sign-on services can seriously increase the consequences of such attacks. In these circumstances strong authentication is mandatory. Strong authentication is often implemented using additional authentication steps or specialized hardware modules, which is not suitable for web-based systems. However, biometrics can used to overcome these limitations. More specifically, behavioural biometrics based on keyboard typing patterns can provide an extra security layer on top of conventional authentication methods, with no additional cost and no impact to the user experience. This work aims to evaluate the feasibility of the implementation of strong authentication on the web using keystroke dynamics. This is carried out through the creation of a web-application prototype, collection of a keystroke dynamics dataset and analysis of various matching algorithms and performance metrics on the collected data.<br>O processo de autenticação é frequentemente referido como a parte mais importante da segurança de um sistema informático. Normalmente, os utilizadores identificam-se utilizando nome de utilizador e palavra-passe, mas este mecanismo nem sempre é suficiente. Considerando serviços baseados na web, ataques como phishing ou engenharia social podem facilmente levar ao roubo de identidade. Para além disso, a utilização crescente de serviços de single sign-on apresenta novos riscos e consequências deste tipo de ataques. Nestas circunstâncias a autenticação forte é fundamental. A autenticação forte é tipicamente implementada por meio de passos adicionais de autenticação ou módulos de hardware especializado, o que não é adequado a sistemas baseados na web. No entanto, biometrias podem ser usadas para ultrapassar estas limitações. Mais especificamente, biometrias comportamentais baseadas em padrões de digitação no teclado podem fornecer um nível de segurança adicional, sem custo acrescido ou impacto na experiência de utilização. Este trabalho tem como objetivo avaliar a viabilidade da implementação de autenticação forte na web usando dinâmica de digitação. Isto é conseguido através da implementação de um protótipo sob a forma de uma aplicação web, captura de dados de digitação e análise de vários algoritmos e métricas de desempenho sobre os dados recolhidos.
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Lin, Tse-Chun, and 林澤君. "Enhanced user authentication by using keystroke dynamics." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/09874732633127629494.

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碩士<br>雲林科技大學<br>資訊管理系碩士班<br>96<br>Purpose: Hwang and Li (2000) proposed a remote user authentication scheme without password table using smart cards based on ID-based cryptosystems. Lee et al. (2004) proposed an improved efficient remote user authentication scheme. However these schemes are vulnerable if the smart card is lost or password eavesdropping. In this article, we try to improve the Lee et al.’s scheme by applying keystroke dynamics and authentication tokens to solve the smart card lost and password eavesdropping problems. Methodology: In this article, we use keystroke dynamic combines with mutual authentication scheme, smart card and RFID to improve the security of the user authentication in network. Finding: After security requirements analyses, our proposed the MKS protocol achieves all the requirements and MKR protocol does not match all requirements. Due to the limitation of RFID tag which can not store user profile. After experiment we find out that the password typing rhythm can influence the FAR significantly. The password contains with words and numbers is better than password only contains words or numbers. Value: Our study is one of the first attempts to apply keystroke dynamic into mutual authentication scheme and combines with smart card and RFID. And this study is one of the first tried to apply keystroke dynamic, smart card and RFID into Kerberos protocol and two-server protocol, respectively.
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Bhatia, Aparna. "Methodologies for user authentication using keystroke dynamics." Thesis, 2018. http://eprint.iitd.ac.in:80//handle/2074/8033.

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LEE, KEN-YU, and 李耕瑜. "On the Neural Networks for Biometric Authentication Based on Keystroke Dynamics." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/p392ph.

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碩士<br>東海大學<br>資訊工程學系<br>105<br>In this study, we propose a biometric authentication method to identify and block illegal users, even if the whole password is exposed. Our method simultaneously records scan codes and keystroke sequence of passwords; furthermore, by deep learning of convolutional neural networks, legal users can be effectively distin- guished from illegal users. The experimental results show that illegal users are successfully blocked even if the password has been exposed. Although the aver- age login failure rate of legal users is 6 percent, they can reenter passwords once to be admitted. We also compare recognition rates between convolutional neural networks and neural networks and prove that convolu- tional neural networks are better. Finally, by GPU parallel computing, we further obtain about 5 times acceleration of system performance.
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Su, Hung-Yu, and 蘇弘毓. "A Study of User Authentication and Cryptographic key Generation Based on Keystroke Dynamics." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/85880660164600500011.

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碩士<br>國立成功大學<br>資訊工程學系碩博士班<br>91<br>The biometrics is to make use of in each living creature individual, can represent that individual, and make use of the not same characteristic in other individual. Reach the function that recognize. Such as fingerprint, iris, sound… etc. And in the computer systems, distinguish the user’s different, only have depend on whether ID and passwords form couples or not to decide. With time passed, intruders also develop lots of way or guessing passwords, to get data or personal privacy originally store in system. Enhancing the password systems. It also with the user''s keystroke dynamics at many researches Proved the user''s keystroke is a repeatable occurrence. And adding user''s keystroke dynamics in password system can use the minimum cost to get the biggest benefit. Besides, in addition to user recognizing, we make use of keystroke dynamics and its property of repeatable to record private key of the user. Only the user input his ID and password, system can restore the key that is not stored in the systems. Then provided for other uses, such as using the public key systems, signature ... etc.
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Wu, Han-Sing, and 吳和興. "A New Approach of Changeable Password for Keystroke Dynamics Authentication System on Smart Phones." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/13641969129536291557.

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碩士<br>國立彰化師範大學<br>數學系所<br>101<br>As time goes by, people rely on cell phones more and more. Cell phones are just a tool to communicate in past, but nowadays besides making a phone call, you can also use cell phones to access e-bank, buy stocks, pay credit card bill and so on. Because the functionality of cell phones is getting powerful, the security of cell phones becomes more important. Most of subscribers use PIN codes which combined with 6~8 numbers to protect their SIM cards from illegal access. It’s an easy method but also easily to be decoded by dictionary attack or shoulder surfing attack. To enhance security, many studies have employed keystroke dynamics to protect the PIN code for the past few years, and the results show that keystroke dynamics can indeed improve the security of a PIN code. However, the traditional keystroke dynamics-based authentication (KDA) system has to collect user’s keystroke dynamics first and then produce a unique personal biometrics of user. It’s inconvenient for users when they want to change PIN code for security reason or other reasons, because they have to retrain the KDA system. To solve above-mentioned problem, this thesis will propose a technique by which users can change their PIN codes anytime without retraining. This technique not only enhance the security of PIN codes but also enhance the security of accessing e-bank, buying stocks, paying credit card bill and other service via cell phones. The experiment result shows that the method proposed in this thesis can help KDA system to distinguish legitimate users or Impostors even when users change their passwords.
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Lai, Dao Yu. "Intelligent online risk-based authentication using Bayesian network model." Thesis, 2011. http://hdl.handle.net/1828/3290.

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Risk-based authentication is an increasingly popular component in the security architecture deployed by many organizations in mitigating online identity threat. Risk-based authentication uses contextual and historical information extracted from online communications to build a risk profile for the user that can be used to make accordingly authentication and authorization decisions. Existing risk-based authentication systems rely on basic web communication information such as the source IP address or the velocity of transactions performed by a specific account, or originating from a certain IP address. Such information can easily be spoofed and as such put in question the robustness and reliability of the proposed systems. In this thesis, we propose in this work an online risk-based authentication system which provides more robust user identity information by combining mouse dynamics, keystroke dynamics biometrics, and user site actions in a multimodal framework. We propose a Bayesian network model for analyzing free keystrokes and mouse movements involved in web sessions. Experimental evaluation of our proposed model with 24 participants yields an Equal Error Rate of 6.91%. This is encouraging considering that we are dealing with free text and mouse movements and the fact that many web sessions tend to be short.<br>Graduate
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Zhong, Po-Kai, and 鍾博凱. "Implementing Multiple Biometric Features to a Recall-based Graphical Keystroke Dynamics Authentication System on Smart Phone." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/52004004627372813524.

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碩士<br>國立彰化師範大學<br>工業教育與技術學系<br>101<br>Keystroke Dynamics-based Authentication (KDA) belongs to a variety of behavioral biometric. It verifies user identity via the keystroke features gathered from the keystroke events on QWERTY keyboard provided by users. With the growing use of smart phones, the traditional keypad on mobile phone had been replaced by touch screen device. The keypad-based KDA has no longer suitable for smart phone. This paper proposed a KDA system implemented by multiple biometric features applied to the pattern lock layout on smart phone. Excluding the time, pressure and size keystroke features presented in previous researches, we additionally adopted a novel angle keystroke feature and determined the best combination of these features in a serial experiment. As the results shown, with 10 training samples involved, the combination of time, pressure and angle presents the best utility (Equal Error Rate of 3.03%).
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20

Silva, Leandro Antonio Oliveira da. "Behavioural biometrics in the World Wide Web." Master's thesis, 2014. http://hdl.handle.net/10316/35527.

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Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia da Universidade de Coimbra<br>Behavioural biometrics such as keystroke dynamics is a reliable human trait that can be used to successfully validate a user claimed identity on a given information system. Apart from its several advantages over traditional authentication mechanisms, the intrinsic time dimension of these distinctive typing patterns also allows for a real time and continuous user authentication. Being the internet security a recurrent and growing concern, this thesis asserts the actual applicability of such behavioural biometrics technique in a web environment, suggesting an elegant and transparent intrusion detection solution that requires no additional hardware or software. The security of users online is ensured just by the mere consequence of typing.
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