Wyniki wyszukiwania - BazTech

1

Hybrid verification method based on finger-knuckle analysis and keystroke dynamics

Wesolowski T. E., Safaverdi H., Doroz R., Wrobel K.

Journal of Medical Informatics & Technologies

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2017

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Vol. 26

26--36

EN

The increasing number of personal data leaks becomes one of the most important security issues hence the need to develop modern computer user verification methods. In the article, a potential of biometric methods fusion for continuous user verification was assessed. A hybrid approach for user verification based on fusion of keystroke dynamics and knuckle images analysis was presented. Verification is performed by a classification module where an ensemble classifier was used to verify the identity of a user. A proposed classifier works on a database which comprises of knuckle images and keyboard events for keystroke dynamics. The proposed approach was tested experimentally. The obtained results confirm that the proposed hybrid approach performs better than methods based on single biometric feature hence the introduced method can be used for increasing a protection level of computer resources against forgers and impostors. The paper presents results of preliminary research conducted to assess the potential of biometric methods fusion.

2

Application of Recurrent Neural Networks for User Verification based on Keystroke Dynamics

Kobojek P., Saeed K.

Journal of Telecommunications and Information Technology

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2016

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nr 3

80--90

EN

Keystroke dynamics is one of the biometrics techniques that can be used for the verification of a human being. This work briefly introduces the history of biometrics and the state of the art in keystroke dynamics. Moreover, it presents an algorithm for human verification based on these data. In order to achieve that, authors’ training and test sets were prepared and a reference dataset was used. The described algorithm is a classifier based on recurrent neural networks (LSTMand GRU). High accuracy without false positive errors as well as high scalability in terms of user count were chosen as goals. Some attempts were made to mitigate natural problems of the algorithm (e.g. generating artificial data). Experiments were performed with different network architectures. Authors assumed that keystroke dynamics data have sequence nature, which influenced their choice of classifier. They have achieved satisfying results, especially when it comes to false positive free setting.

3

Person verification based on keystroke dynamics

Doroz R., Porwik P., Safaverdi H.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

39--44

EN

This paper presents a new multilayer ensemble of classifiers for users verification who use computer keyboard. The special keyboard extracts the key pressure and latency between keyboard keys pressed during password entered. When user is typing password the system creates a pattern based on time and key pressure. For users verification group of classifiers have been proposed. It allows to obtain the higher accuracy level compared to alternative techniques. The efficiency of the proposed method has been confirmed in the experiments carried out.

4

An approach to classify keystroke patterns for remote user authentication

Saha J., Chaki R

Journal of Medical Informatics & Technologies

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2014

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Vol. 23

141--148

EN

The authentication of users is of utmost importance in remote applications such as healthcare, banking, stock markets, etc. Key stroke dynamics are popular biometrics tools used for this purpose. Continuous authentication requires free text analysis which has a number of challenges. This paper has proposed a solution to identify the existence of a unique pattern in each individual user’s keystroke dynamics. However, dense zone identification is important factor in forming the intelligent database of user profile for authentication. The authors have categorized basic key stroke features of digraph into 57 groups depending on distance traversed while moving from one key to another. The paper also includes graphical plots of the grouping of time vector which has unveiled some characteristics of overlapping typing style of users. The authors hope to extend this logic for identifying behavioral disorders in users.

5

Behawioralne metody biometryczne - dynamika pisania na klawiaturze

Długosz T., Wujczyk P.

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2009

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nr 10

1965-1968

PL

Przedstawiono koncepcję i praktyczną realizację silnej metody uwierzytelniania, jaką jest dynamika pisania na klawiaturze, oparta na unikalnych cechach każdego człowieka. Dynamika jest wykorzystana głównie w weryfikacji, ale także może być stosowana do identyfikacji. W pracy przedstawiono oryginalne wyniki z badań przeprowadzonych przez autorów.

EN

This paper is devoted to an idea and practical implementation of strong biometric method which is based on assumption that people type in uniquely characteristic manners. Keystroke dynamics is mainly used for verification but it can be used for identification too. In the paper original test results are presented.