Improved Keystroke Authentication Accuracy Based on Statistics and Weight

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,

Keystroke Authentication on Enhanced Needleman Alignment Algorithm

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.

Application of a Dynamic Identity Authentication Model Based on an Improved Keystroke Rhythm Algorithm

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.

Keystroke Dynamics Based Authentication Using Possibilistic Renyi Entropy Features and Composite Fuzzy Classifier

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.

Profile Generation Methods for Reinforcing Robustness of Keystroke Authentication in Free Text Typing

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.

Keyboard Dependency of Personal Identification Performance by Keystroke Dynamics in Free Text Typing

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.

Performance Evaluation of an Anomaly-Detection Algorithm for Keystroke-Typing Based Insider Detection

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.

TKCA:a timely keystroke-based continuous user authentication with short keystroke sequence in uncontrolled settings

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.

User Interface-Based Repeated Sequence Detection Method for Authentication

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.