An Exploration of Keystroke Dynamics Authentication Using Non-fixed Text of Various Length

Rybnik, Mariusz; Tabędzki, Marek; Adamski, Marcin; Saeed, Khalid

doi:10.1109/icbake.2013.48

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…Many existing solutions are based on statistical data on the specific events, the number of their occurrences in time, or simultaneous occurrences of a chosen set of them [11]. The most frequently used characteristics include: the duration of a specific keypress and intervals between keypresses, typing speed (the average number of keystrokes in a given time), overlapping of a certain key combinations, ratio of Shift or Capslock buttons usage to type upper/lowercase letters, number of errors, error correction methods, and the use of the navigation (arrow) keys for the cursor.…”

Section: The State Of the Art In Keystroke Dynamics Identificationmentioning

confidence: 99%

“…The most frequently used characteristics include: the duration of a specific keypress and intervals between keypresses, typing speed (the average number of keystrokes in a given time), overlapping of a certain key combinations, ratio of Shift or Capslock buttons usage to type upper/lowercase letters, number of errors, error correction methods, and the use of the navigation (arrow) keys for the cursor. Only two features and a simple classifier can allow for quite effective authorization or identification of the user [11].…”

Section: The State Of the Art In Keystroke Dynamics Identificationmentioning

confidence: 99%

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Biometric Identification Based on Keystroke Dynamics

Kasprowski

Borowska

Harężlak

2022

Sensors

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The purpose of the paper is to study how changes in neural network architecture and its hyperparameters affect the results of biometric identification based on keystroke dynamics. The publicly available dataset of keystrokes was used, and the models with different parameters were trained using this data. Various neural network layers—convolutional, recurrent, and dense—in different configurations were employed together with pooling and dropout layers. The results were compared with the state-of-the-art model using the same dataset. The results varied, with the best-achieved accuracy equal to 82% for the identification (1 of 20) task.

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Section: The State Of the Art In Keystroke Dynamics Identificationmentioning

confidence: 99%

Section: The State Of the Art In Keystroke Dynamics Identificationmentioning

confidence: 99%

Biometric Identification Based on Keystroke Dynamics

Kasprowski

Borowska

Harężlak

2022

Sensors

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“…However, after nearly ten years of development, Shimpshon et al [22] proposed a clustering method based on graph in 2010 which added a similar continuous keystroke to form a fixed length of the session, and the experimental results in 21 real users and 165 counterfeiters showed that it has a False Accept Rate (FAR) of 3.47% just by using 250 keystrokes. More specifically, Rybnik et al [23] explored keystrokes in different lengths of nonfixed text in 2013, providing a reliable basis for the authentication of free text. After that, Song et al [24] constructed a Gaussian model for the user's recent input characters sequence based on the Gaussian probability density function in 2016, which shortened the authentication cycle and reached FAR of 5.3% under 30 characters.…”

Section: Keystroke-based Authentication Schemesmentioning

confidence: 99%

Design and Implementation of Continuous Authentication Mechanism Based on Multimodal Fusion Mechanism

Guan

Zhang

2021

Security and Communication Networks

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Most of the current authentication mechanisms adopt the “one-time authentication,” which authenticate users for initial access. Once users have been authenticated, they can access network services without further verifications. In this case, after an illegal user completes authentication through identity forgery or a malicious user completes authentication by hijacking a legitimate user, his or her behaviour will become uncontrollable and may result in unknown risks to the network. These kinds of insider attacks have been increasingly threatening lots of organizations, and have boosted the emergence of zero trust architecture. In this paper, we propose a Multimodal Fusion-based Continuous Authentication (MFCA) scheme, which collects multidimensional behaviour characteristics during the online process, verifies their identities continuously, and locks out the users once abnormal behaviours are detected to protect data privacy and prevent the risk of potential attack. More specifically, MFCA integrates the behaviours of keystroke, mouse movement, and application usage and presents a multimodal fusion mechanism and trust model to effectively figure out user behaviours. To evaluate the performance of the MFCA, we designed and implemented the MFCA system and the experimental results show that the MFCA can detect illegal users in quick time with high accuracy.

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“…The datasets in the literature contain keystroke data, which in most cases, are collected from users typing 2-3 sentences only [36]. Rybnik et al [37] created a free text dataset, which contains keystrokes from 9 volunteers. Each volunteer typed a long text of more than 250 characters twice in five sessions.…”

Section: Related Workmentioning

confidence: 99%

R²BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification

Tsimperidis

Yoo

Taha

et al. 2020

IEEE Trans. Cybern.

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Over the past decade keystroke-based pattern recognition techniques as a forensic tool for behavioural biometrics have gained increasing attention. Although a number of machine learning based approaches have been proposed, they are limited in terms of their capability to recognise and profile a set of individual's characteristics. In addition, up to today their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other characteristics, such as the educational level. Educational level is an acquired user characteristic, which can improve targeted advertising, as well as provide valuable information in a digital forensic investigation, when it is known. In this context, this paper proposes a novel machine learning model, the Randomised Radial Basis function Network (R 2 BN), which recognises and profiles the educational level of an individual who stands behind the keyboard. The performance of the proposed model is evaluated by using empirical data obtained by recording volunteers' keystrokes during their daily usage of a computer. Its performance is also compared with other wellreferenced machine learning models using our keystroke dynamic datasets. Although the proposed model achieves high accuracy in educational level prediction of an unknown user, it suffers from high computational cost. For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the trade off between an accurate and a fast prediction. To the best of our knowledge, this is the first model in the literature that predicts the educational level of an individual based on keystroke dynamics information only.

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An Exploration of Keystroke Dynamics Authentication Using Non-fixed Text of Various Length

Cited by 12 publications

References 12 publications

Biometric Identification Based on Keystroke Dynamics

Biometric Identification Based on Keystroke Dynamics

Design and Implementation of Continuous Authentication Mechanism Based on Multimodal Fusion Mechanism

R²BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification

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An Exploration of Keystroke Dynamics Authentication Using Non-fixed Text of Various Length

Cited by 12 publications

References 12 publications

Biometric Identification Based on Keystroke Dynamics

Biometric Identification Based on Keystroke Dynamics

Design and Implementation of Continuous Authentication Mechanism Based on Multimodal Fusion Mechanism

R2BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification

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R²BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification