Temat: analiza klawiszy - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: User verification based on the analysis of keystrokes while using various software
Autorzy:: Wesołowski, T. E.
Porwik, P.
Powiązania:: https://bibliotekanauki.pl/articles/333083.pdf
Data publikacji:: 2015
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: keystroke analysis
free text analysis user profile
intruder detection
user verification
analiza klawiszy
analiza tekstu
wykrywanie intruza
weryfikacja użytkownika
Opis:: The article presents the new approach to a computer users verification. The research concerns an analysis of user’s continuous activity related to a keyboard used while working with various software. This type of analysis constitutes a type of free-text analysis. The presented method is based on the analysis of users activity while working with particular computer software (e.g. text editors, utilities). A method of computer user proﬁling is proposed and an attempt to intrusion detection based on k-NN classifier is performed. The obtained results show that the introduced method can be used in the intrusion detection and monitoring systems. Such systems are especially needed in medical facilities where sensitive data are processed.
Źródło:: Journal of Medical Informatics & Technologies; 2015, 24; 13-22
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Bio-authentication for layered remote health monitor framework
Autorzy:: Bhattasali, T.
Saeed, K.
Chaki, N.
Chaki, R
Powiązania:: https://bibliotekanauki.pl/articles/333249.pdf
Data publikacji:: 2014
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: remote health monitor
security issues
multi-factor biometric authentication
keystroke analysis
face recognition
zdalne monitorowanie zdrowia
bezpieczeństwo
uwierzytelnianie biometryczne wieloczynnikowe
analiza klawiszy
rozpoznawanie twarzy
Opis:: Aged people, patients with chronic disease, patients at remote location need continuous monitoring under healthcare professionals. Remote health monitor is likely to be an effective approach to provide healthcare service in a simple and cost effective way. However, effective implementation of this type of framework needs consideration of variety of security threats. In this paper, a layer based remote health monitor framework is proposed to analyze health condition of patients from remote places. Beside this, a multi-modal biometric authentication mechanism is proposed here to reduce misuse of health data and biometrics templates in heterogeneous cloud environment. Main focus of the paper is to design semi-continuous authentication mechanism after establishing mutual 1:1 trust relationship among the participants in cloud environment. Behavioral biometrics keystroke analysis is fused with physiological biometrics face recognition to enhance accuracy of authentication. Instead of considering traditional performance evaluation parameters for biometrics, this paper considers a few performance metrics for determining efficiency of semi-continuous verification of the proposed framework.
Źródło:: Journal of Medical Informatics & Technologies; 2014, 23; 131-139
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

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

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