An important point for computer systems is the identification of users for authentication. One of these identification methods is keystroke dynamics. The keystroke dynamics is a biometric measurement in terms of keyst...An important point for computer systems is the identification of users for authentication. One of these identification methods is keystroke dynamics. The keystroke dynamics is a biometric measurement in terms of keystroke press duration and keystroke latency. However, several problems are arisen like the similarity between users and identification accuracy. In this paper, we propose innovative model that can help to solve the problem of similar user by classifying user’s data based on a membership function. Next, we employ sequence alignment as a way of pattern discovery from the user’s typing behaviour. Experiments were conducted to evaluate accuracy of the proposed model. The results show high performance compared to standard classifiers in terms of accuracy and precision.展开更多
Keystroke rhythm identification, which extracts biometric characteristics through keyboards without addi-tional expensive devices, is a kind of biometric identification technology. The paper proposes a dynamic identit...Keystroke rhythm identification, which extracts biometric characteristics through keyboards without addi-tional expensive devices, is a kind of biometric identification technology. The paper proposes a dynamic identity authentication model based on the improved keystroke rhythm algorithm in Rick Joyce model and implement this model in a mobile phone system. The experimental results show that comparing with the original model, the false alarm rate (FAR) of the improved model decreases a lot in the mobile phone system, and its growth of imposter pass rate (IPR) is slower than the Rick Joyce model’s. The improved model is more suitable for small memory systems, and it has better performance in security and dynamic adaptation. This improved model has good application value.展开更多
This paper presents the formulation of the possibilistic Renyi entropy function from the Renyi entropy function using the framework of Hanman-Anirban entropy function. The new entropy function is used to derive the in...This paper presents the formulation of the possibilistic Renyi entropy function from the Renyi entropy function using the framework of Hanman-Anirban entropy function. The new entropy function is used to derive the information set features from keystroke dynamics for the authentication of users. A new composite fuzzy classifier is also proposed based on Mamta-Hanman entropy function and applied on the Information Set based features. A comparison of the results of the proposed approach with those of Support Vector Machine and Random Forest classifier shows that the new classifier outperforms the other two.展开更多
We have investigated several characteristics of the keystroke authentication in Japanese free text typing, and our methods have provided high recognition accuracy for high typing skill users who can type 700 or more l...We have investigated several characteristics of the keystroke authentication in Japanese free text typing, and our methods have provided high recognition accuracy for high typing skill users who can type 700 or more letters per 5 minutes. There are, however, some situations decreasing the accuracy such as long period passage after registering each user’s profile documents and existence of lower typing skill users who can type only about 500 - 600 letters per 5 minutes. In this paper, we propose new profile generation methods, profile-updating and profile-combining methods, to reinforce the robustness of keystroke authentication and show the effectiveness of them through three examinations with experimental data.展开更多
We have proposed some methods for feature extraction and identification that enable identification of individuals through long-text input as an important topic in keystroke dynamics research. As to the robustness in p...We have proposed some methods for feature extraction and identification that enable identification of individuals through long-text input as an important topic in keystroke dynamics research. As to the robustness in practical circumstances, there exists a question on the keystroke dynamics how much the recognition accuracy is influenced by the change of keyboard. By comparing the performance in the cases of using the same keyboard and different keyboards, the dependencies on keyboards are evaluated through three implemented experiments for subjects. As a result, it is found that we do not need to worry about the keyboard difference for users whose typing skills reach high level with about 900 or more letters in 5 minutes, and only for the remaining users it would be necessary to register their profile data with respect to each keyboard they use in order to avoid recognition accuracy degradation.展开更多
Keystroke-based behavioral biometrics have been proven effective for continuous user authentication.Current state-of-the-art algorithms have achieved outstanding results in long text or short text collected by doing s...Keystroke-based behavioral biometrics have been proven effective for continuous user authentication.Current state-of-the-art algorithms have achieved outstanding results in long text or short text collected by doing some tasks.It remains a considerable challenge to authenticate users continuously and accurately with short keystroke inputs collected in uncontrolled settings.In this work,we propose a Timely Keystroke-based method for Continuous user Authentication,named TKCA.It integrates the key name and two kinds of timing features through an embedding mechanism.And it captures the relationship between context keystrokes by the Bidirectional Long Short-Term Memory(Bi-LSTM)network.We conduct a series of experiments to validate it on a public dataset-the Clarkson II dataset collected in a completely uncontrolled and natural setting.Experiment results show that the proposed TKCA achieves state-of-the-art performance with 8.28%of EER when using only 30 keystrokes and 2.78%of EER when using 190 keystrokes.展开更多
In this paper,we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security.The proposed method identifies personalized repeated user interface(UI)sequences by analyzi...In this paper,we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security.The proposed method identifies personalized repeated user interface(UI)sequences by analyzing mouse and keyboard data.To this end,an Apriori algorithm based on the keystroke-level model(KLM)of the human–computer interface domain was used.The proposed system can detect repeated UI sequences based on KLM for authentication in the software.The effectiveness of the proposed method is verified through access test-ing using commercial applications that require intensive UI interactions.The results show using our cognitive mouse-and-keystroke dynamics system can com-plement authentication at the application level.展开更多
文摘An important point for computer systems is the identification of users for authentication. One of these identification methods is keystroke dynamics. The keystroke dynamics is a biometric measurement in terms of keystroke press duration and keystroke latency. However, several problems are arisen like the similarity between users and identification accuracy. In this paper, we propose innovative model that can help to solve the problem of similar user by classifying user’s data based on a membership function. Next, we employ sequence alignment as a way of pattern discovery from the user’s typing behaviour. Experiments were conducted to evaluate accuracy of the proposed model. The results show high performance compared to standard classifiers in terms of accuracy and precision.
文摘Keystroke rhythm identification, which extracts biometric characteristics through keyboards without addi-tional expensive devices, is a kind of biometric identification technology. The paper proposes a dynamic identity authentication model based on the improved keystroke rhythm algorithm in Rick Joyce model and implement this model in a mobile phone system. The experimental results show that comparing with the original model, the false alarm rate (FAR) of the improved model decreases a lot in the mobile phone system, and its growth of imposter pass rate (IPR) is slower than the Rick Joyce model’s. The improved model is more suitable for small memory systems, and it has better performance in security and dynamic adaptation. This improved model has good application value.
文摘This paper presents the formulation of the possibilistic Renyi entropy function from the Renyi entropy function using the framework of Hanman-Anirban entropy function. The new entropy function is used to derive the information set features from keystroke dynamics for the authentication of users. A new composite fuzzy classifier is also proposed based on Mamta-Hanman entropy function and applied on the Information Set based features. A comparison of the results of the proposed approach with those of Support Vector Machine and Random Forest classifier shows that the new classifier outperforms the other two.
文摘We have investigated several characteristics of the keystroke authentication in Japanese free text typing, and our methods have provided high recognition accuracy for high typing skill users who can type 700 or more letters per 5 minutes. There are, however, some situations decreasing the accuracy such as long period passage after registering each user’s profile documents and existence of lower typing skill users who can type only about 500 - 600 letters per 5 minutes. In this paper, we propose new profile generation methods, profile-updating and profile-combining methods, to reinforce the robustness of keystroke authentication and show the effectiveness of them through three examinations with experimental data.
文摘We have proposed some methods for feature extraction and identification that enable identification of individuals through long-text input as an important topic in keystroke dynamics research. As to the robustness in practical circumstances, there exists a question on the keystroke dynamics how much the recognition accuracy is influenced by the change of keyboard. By comparing the performance in the cases of using the same keyboard and different keyboards, the dependencies on keyboards are evaluated through three implemented experiments for subjects. As a result, it is found that we do not need to worry about the keyboard difference for users whose typing skills reach high level with about 900 or more letters in 5 minutes, and only for the remaining users it would be necessary to register their profile data with respect to each keyboard they use in order to avoid recognition accuracy degradation.
基金the National Key R&D Program of China(Grant No.2016YFB0801002).
文摘Keystroke-based behavioral biometrics have been proven effective for continuous user authentication.Current state-of-the-art algorithms have achieved outstanding results in long text or short text collected by doing some tasks.It remains a considerable challenge to authenticate users continuously and accurately with short keystroke inputs collected in uncontrolled settings.In this work,we propose a Timely Keystroke-based method for Continuous user Authentication,named TKCA.It integrates the key name and two kinds of timing features through an embedding mechanism.And it captures the relationship between context keystrokes by the Bidirectional Long Short-Term Memory(Bi-LSTM)network.We conduct a series of experiments to validate it on a public dataset-the Clarkson II dataset collected in a completely uncontrolled and natural setting.Experiment results show that the proposed TKCA achieves state-of-the-art performance with 8.28%of EER when using only 30 keystrokes and 2.78%of EER when using 190 keystrokes.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF),funded by the Ministry of Education(2021R1I1A3058103,2019R1A2C1002525).
文摘In this paper,we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security.The proposed method identifies personalized repeated user interface(UI)sequences by analyzing mouse and keyboard data.To this end,an Apriori algorithm based on the keystroke-level model(KLM)of the human–computer interface domain was used.The proposed system can detect repeated UI sequences based on KLM for authentication in the software.The effectiveness of the proposed method is verified through access test-ing using commercial applications that require intensive UI interactions.The results show using our cognitive mouse-and-keystroke dynamics system can com-plement authentication at the application level.
