Applications of keystroke analysis for improved login security and continuous user authentication

Furnell, Steven; Morrissey, J; Sanders, Peter; Stockel, C. T.

doi:10.1007/978-1-5041-2919-0_25

Cited by 23 publications

(8 citation statements)

References 6 publications

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“…Table 9 depicts the comparative studies of the proposed KDA and the existing methods. From Table 9, it is observed that the proposed KDA method has the lowest FRR (1%) and FAR (0%) values compared to the other existing methods (Furnell et al, 1996;Gunetti & Picardi, 2005;Kim et al, 2018;Leggett et al, 1991;Mondal & Bours, 2017). These rates are optimal for real-time implementation.…”

Section: Comparative Studymentioning

confidence: 93%

“…The KDA method can be implemented in two distinct ways: static and dynamic (Furnell, Morrissey, Sanders, & Stockel, 1996;Gunetti & Picardi, 2005). The idea of different keystroke sequences for a single word has been introduced to seek the typing variance between various keystroke sequences of the word (Syed, Banerjee, & Cukic, 2016).…”

Section: Keystroke Dynamics Based Authenticationmentioning

confidence: 99%

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Applying Empirical Thresholding Algorithm for a Keystroke Dynamics Based Authentication System

Venugopal

Viji²

2019

JICT

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Through the application of a password-based authentication technique, users are granted permission to access a secure system when the username and password matches with that logged in database of the system. Furthermore, anyone who provides the correct username and password of a valid user will be able to log in to the secure network. In current circumstances, impostors can hack the system to obtain a user's password, while it has also been easy to find out a person's private password. Thus, the existing structure is exceptionally flawed. One way to strengthen the password-based authentication technique, is by keystroke dynamics. In the proposed keystroke dynamics based authentication system, despite the password match, the similarity between the typing pattern of the typed password and password samples in the training database are verified. The timing features of the user's keystroke dynamics are collected to calculate the threshold values. In this paper, a novel algorithm is proposed to authenticate the legal users based on the empirical threshold values. The first step involves the extraction of timing features from the typed password samples. The password training database for each user is constructed using the extracted features. Moreover, the empirical threshold limits are calculated from the timing features in the database. The second step involves user authentication by applying these threshold values.

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Section: Comparative Studymentioning

confidence: 93%

Section: Keystroke Dynamics Based Authenticationmentioning

confidence: 99%

Applying Empirical Thresholding Algorithm for a Keystroke Dynamics Based Authentication System

Venugopal

Viji²

2019

JICT

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“…Due to the instability of typing rhythms, the incipient studies utilized a long predefined text in making a template of the user [25,18]. Though it is tedious that the system requires a user to type too much, a long predefined text is used for diverse purposes [10,2]. Most research papers utilize relatively short strings such as first name, last name or password, but some research use a free text for extracting the typing characteristics of a particular user [21,9,13].…”

Section: Previous Workmentioning

confidence: 99%

“…Statistical methods using averages and standard deviations are most popular [25,18,16,10,9,14]. Some researchers applied fuzzy logic to the measured values of keystroke tim- ings [8,1], and there are also neural network-based methods [3,19,4].…”

Section: Previous Workmentioning

confidence: 99%

An evolutionary keystroke authentication based on ellipsoidal hypothesis space

Lee

Choi

Moon

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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Keystroke authentication is a biometric method utilizing the typing characteristics of users. In this paper, we propose an evolutionary method for stable keystroke authentication. In the method, typing characteristics of users are represented by n-dimensional vectors and an ellipsoidal hypothesis space, which distinguishes a collection of the timing vectors of a user from those of the others, is evolved by a genetic algorithm. A filtering scheme and an adaptation mechanism are also presented to improve the stability and effectiveness of the proposed method. Empirical results show that the error rates of our method for authentication are reasonably small.

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“…Each person typed the same text samples at two different time periods, separated by several days. A KDA technique had been introduced in (Furnell et al, 1996), and verified both static and dynamic modes. In static mode,15 participants were used to collect text samples.…”

Section: Comparative Studymentioning

confidence: 99%

Applying Empirical Thresholding Algorithm for a Keystroke Dynamics Based Authentication System

Venugopal¹,

Viji²

2019

Journal of Information and Communication Technology

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Through the application of a password-based authentication technique, users are granted permission to access a secure system when the username and password matches with that logged in database of the system. Furthermore, anyone who provides the correct username and password of a valid user will be able to log in to the secure network. In current circumstances, impostors can hack the system to obtain a user’s password, while it has also been easy to find out a person’s private password. Thus, the existing structure is exceptionally flawed. One way to strengthen the password-based authentication technique, is by keystroke dynamics. In the proposed keystroke dynamics based authentication system, despite the password match, the similarity between the typing pattern of the typed password and password samples in the training database are verified. The timing features of the user’s keystroke dynamics are collected to calculate the threshold values. In this paper, a novel algorithm is proposed to authenticate the legal users based on the empirical threshold values. The first step involves the extraction of timing features from the typed password samples. The password training database for each user is constructed using the extracted features. Moreover, the empirical threshold limits are calculated from the timing features in the database. The second step involves user authentication by applying these threshold values. The experimental analyses are carried out in MATLAB simulation, and the results indicate a significant reduction in false rejection rate and false acceptance rate. The proposed methodology yields very low equal error rate of 0.5% and the authentication accuracy of 99.5%, which are considered suitable and efficient for real-time implementation. The proposed method can be a useful resource for identifying illegal invasion and is valuable in securing the system as a correlative or substitute form of client validation.

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Applications of keystroke analysis for improved login security and continuous user authentication

Cited by 23 publications

References 6 publications

Applying Empirical Thresholding Algorithm for a Keystroke Dynamics Based Authentication System

Applying Empirical Thresholding Algorithm for a Keystroke Dynamics Based Authentication System

An evolutionary keystroke authentication based on ellipsoidal hypothesis space

Applying Empirical Thresholding Algorithm for a Keystroke Dynamics Based Authentication System

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