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

Bhatia, Aparna; Hanmandlu, M.

doi:10.4236/jmp.2018.92008

Cited by 7 publications

(2 citation statements)

References 20 publications

(19 reference statements)

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“…This classifier employs several rules based on the training samples. A study [303] shows that the composite fuzzy classifier outperforms SVM and RF. Similarly, a study [304] compared fuzzy classifier with other previously proposed four ap-proaches and found that the classifier is more impressive than others.…”

Section: ) Binary Classificationsmentioning

confidence: 99%

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

et al. 2022

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The purpose of this study is to conduct a comprehensive evaluation and analysis of the most recent studies on the implications of keystroke dynamics (KD) patterns in user authentication, identification, and the determination of useful information. Another aim is to provide an extensive and up-to-date survey of the recent literature and potential research directions to understand the present state-of-the-art methodologies in this particular domain that are expected to be beneficial for the KD research community. From January 1st, 2017 to March 13th, 2022, the popular six electronic databases have been searched using a search criterion ("keystroke dynamics" OR "typing pattern") AND ("authentication" OR "verification" OR "identification"). With this criterion, a total of nine thousand three hundred forty-eight results, including duplicates, were produced. However, one thousand five hundred forty-seven articles have been chosen after removing duplicates and preliminary screening. Due to insufficient information, only one hundred twentyseven high-quality quantitative research articles have been included in the article selection process. We compared and summarised several factors with multiple tables to comprehend the various methodologies, experimental settings, and findings. In this study, we have identified six unique KD-based designs and presented the status of findings toward an effective solution in authentication, identification, and prediction. We have also discovered considerable heterogeneity across studies in each KD-based design for desktops and smartphones separately. Finally, this paper found a few open research challenges and provided some indications for a deeper understanding of the issues and further study.

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Section: ) Binary Classificationsmentioning

confidence: 99%

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

et al. 2022

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“…At the beginning of the KDA-related studies, researchers focused on applying KD to fixedlength and fixed-content data such as usernames and passwords [43]. Classifiers such as nearest neighbor classifiers [27], statistical classifiers [43,53], neural network classifiers [3], ranking-based classifiers [17], and fuzzy-based classifiers [11] were used to verify users. Recently, many researchers have turned focus on long text verification [49,55].…”

Section: Introductionmentioning

confidence: 99%

New soft biometrics for limited resource in keystroke dynamics authentication

Chang

Tsai

Yeh

et al. 2020

Multimed Tools Appl

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In recent years, some researches on free text authentication by keystroke dynamics have been proposed. The main problem of these proposed researches is the requirement of a very long training time. To increase users' willingness to use the proposed authentication system, one simple and feasible way is shortening the training time. In this case, the training data is limited and is known as the limited resource problem. In this paper, we propose new soft biometrics and a new classifier for limited resources in free text authentication in English. Our new soft biometrics combines the idea of data mining and statistical prediction. Because our soft biometric is a mining result of limited resources, it is sensitive to outliers and a traditional statistical classifier cannot be applied. To solve this problem, the proposed classifiers considered the problem of an outlier and calculated the difference of cluster distribution. There are 114 participants in our experiments. Experimental results show that our approach can improve the accuracy of free text authentication in the case of limited resources.

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