Abstract： In order to improve the recognition accuracy of key stroke authentication, a methodology based on feature extraction of keystroke sequence is presented in this paper. Firstly, the data of the users＇ keystr...Abstract： In order to improve the recognition accuracy of key stroke authentication, a methodology based on feature extraction of keystroke sequence is presented in this paper. Firstly, the data of the users＇ keystroke feature information that has too much deviation with the mean deviation is filtered out. Secondly, the probability of each input key is calculated and 10 values which do not have the best features are selected. Thirdly, they are weighed and a score evaluating the extent to which the user could be authenticated successfully is calculated. The benefit of using a third-party data set is more objective and comparable. At last,展开更多
Keystroke dynamics is the process to identify or authenticate individuals based on their typing rhythm behaviors. Several classifications have been proposed to verify a user＇s legitimacy, and the performances of thes...Keystroke dynamics is the process to identify or authenticate individuals based on their typing rhythm behaviors. Several classifications have been proposed to verify a user＇s legitimacy, and the performances of these classifications should be confirmed to identify the most promising research direction. However, classification research contains several experiments with different conditions such as datasets and methodologies. This study aims to benchmark the algorithms to the same dataset and features to equally measure all performances. Using a dataset that contains the typing rhythm of 51 subjects, we implement and evaluate 15 classifiers measured by Fl-measure, which is the harmonic mean of a false-negative identification rate and false-positive identification rate. We also develop a methodology to process the typing data. By considering a case in which the model will reject the outsider, we tested the algorithms on an open set. Additionally, we tested different parameters in random forest and k nearest neighbors classifications to achieve better results and explore the cause of their high performance. We also tested the dataset on one-class classification and explained the results of the experiment. The top-performing classifier achieves an Fl-measure rate of 92% while using the normalized typing data of 50 subjects to train and the remaining data to test. The results, along with the normalization methodology, constitute a benchmark for comparing the classifiers and measuring the performance of keystroke dynamics for insider detection.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
基金This paper has been performed in the Project "Key Technology Research of Eavesdropping Detection in the Quantum Security Communication" supported by the National Natural Science Foundation of China
文摘Abstract： In order to improve the recognition accuracy of key stroke authentication, a methodology based on feature extraction of keystroke sequence is presented in this paper. Firstly, the data of the users＇ keystroke feature information that has too much deviation with the mean deviation is filtered out. Secondly, the probability of each input key is calculated and 10 values which do not have the best features are selected. Thirdly, they are weighed and a score evaluating the extent to which the user could be authenticated successfully is calculated. The benefit of using a third-party data set is more objective and comparable. At last,
基金supported by the National Natural Science Foundation of China (Nos. 61403301 and 61773310)the China Postdoctoral Science Foundation (Nos. 2014M560783 and 2015T81032)+1 种基金the Natural Science Foundation of Shaanxi Province (No. 2015JQ6216)the Fundamental Research Funds for the Central Universities (No. xjj2015115)
文摘Keystroke dynamics is the process to identify or authenticate individuals based on their typing rhythm behaviors. Several classifications have been proposed to verify a user＇s legitimacy, and the performances of these classifications should be confirmed to identify the most promising research direction. However, classification research contains several experiments with different conditions such as datasets and methodologies. This study aims to benchmark the algorithms to the same dataset and features to equally measure all performances. Using a dataset that contains the typing rhythm of 51 subjects, we implement and evaluate 15 classifiers measured by Fl-measure, which is the harmonic mean of a false-negative identification rate and false-positive identification rate. We also develop a methodology to process the typing data. By considering a case in which the model will reject the outsider, we tested the algorithms on an open set. Additionally, we tested different parameters in random forest and k nearest neighbors classifications to achieve better results and explore the cause of their high performance. We also tested the dataset on one-class classification and explained the results of the experiment. The top-performing classifier achieves an Fl-measure rate of 92% while using the normalized typing data of 50 subjects to train and the remaining data to test. The results, along with the normalization methodology, constitute a benchmark for comparing the classifiers and measuring the performance of keystroke dynamics for insider detection.
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.
