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Wyszukujesz frazę "keystroke dynamics" wg kryterium: Temat


Wyświetlanie 1-5 z 5
Tytuł:
Application of Recurrent Neural Networks for User Verification based on Keystroke Dynamics
Autorzy:
Kobojek, P.
Saeed, K.
Powiązania:
https://bibliotekanauki.pl/articles/307650.pdf
Data publikacji:
2016
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
biometrics
GRU networks
keystroke dynamics
LSTM networks
recurrent neural networks
user verification
Opis:
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.
Źródło:
Journal of Telecommunications and Information Technology; 2016, 3; 80-90
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Person verification based on keystroke dynamics
Autorzy:
Doroz, R.
Porwik, P.
Safaverdi, H.
Powiązania:
https://bibliotekanauki.pl/articles/334042.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
keystroke dynamics
ensemble of classifiers
biometrics
dynamika pisania na klawiaturze
zespół klasyfikatorów
biometria
Opis:
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.
Źródło:
Journal of Medical Informatics & Technologies; 2015, 24; 39-44
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid verification method based on finger-knuckle analysis and keystroke dynamics
Autorzy:
Wesolowski, T. E.
Safaverdi, H.
Doroz, R.
Wrobel, K.
Powiązania:
https://bibliotekanauki.pl/articles/333719.pdf
Data publikacji:
2017
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
user verification
keystroke dynamics
finger-knuckle analysis
weryfikacja użytkownika
dynamika pisania na klawiaturze
analiza palców
Opis:
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.
Źródło:
Journal of Medical Informatics & Technologies; 2017, 26; 26-36
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An approach to classify keystroke patterns for remote user authentication
Autorzy:
Saha, J.
Chaki, R
Powiązania:
https://bibliotekanauki.pl/articles/333293.pdf
Data publikacji:
2014
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
free text
keystroke dynamics
biometrics
digraph grouping
dense area identification
tekst
dynamika pisania na klawiaturze
biometria
struktura dwuznakowa
digraf
Opis:
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.
Źródło:
Journal of Medical Informatics & Technologies; 2014, 23; 141-148
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Keystroke dynamics analysis using machine learning methods
Autorzy:
Shabliy, Nataliya
Lupenko, Serhii
Lutsyk, Nadiia
Yasniy, Oleh
Malyshevska, Olha
Powiązania:
https://bibliotekanauki.pl/articles/1956034.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
keystroke dynamics analysis
Machine Learning
Neural Network
Supervised Learning
classification problem
analiza dynamiki uderzeń klawiszy
uczenie maszynowe
sieć neuronowa
uczenie nadzorowane
problem klasyfikacji
Opis:
The primary objective of the paper was to determine the user based on its keystroke dynamics using the methods of machine learning. Such kind of a problem can be formulated as a classification task. To solve this task, four methods of supervised machine learning were employed, namely, logistic regression, support vector machines, random forest, and neural network. Each of three users typed the same word that had 7 symbols 600 times. The row of the dataset consists of 7 values that are the time period during which the particular key was pressed. The ground truth values are the user id. Before the application of machine learning classification methods, the features were transformed to z-score. The classification metrics were obtained for each applied method. The following parameters were determined: precision, recall, f1-score, support, prediction, and area under the receiver operating characteristic curve (AUC). The obtained AUC score was quite high. The lowest AUC score equal to 0.928 was achieved in the case of linear regression classifier. The highest AUC score was in the case of neural network classifier. The method of support vector machines and random forest showed slightly lower results as compared with neural network method. The same pattern is true for precision, recall and F1-score. Nevertheless, the obtained classification metrics are quite high in every case. Therefore, the methods of machine learning can be efficiently used to classify the user based on keystroke patterns. The most recommended method to solve such kind of a problem is neural network.
Źródło:
Applied Computer Science; 2021, 17, 4; 75-83
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-5 z 5

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