Machine Learning Algorithm on Keystroke Dynamics Pattern

Baynath, Purvashi; Soyjaudah, K. M. S.; Khan, Maleika Heenaye-Mamode

doi:10.1109/spc.2018.8704135

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…The solution has achieved err as low as 5.32%. A different study [24] used .tie5Roalnb , .aeihoz246@ , .nzkla29zah.# , and aeR5t.ilnb passwords to acquire 30,000 samples from 1000 voluntary subjects. The dataset was used to train several classifiers, demonstrating accuracy of >98 % and an ability to manage large datasets.…”

Section: Related Workmentioning

confidence: 99%

Password policy characteristics and keystroke biometric authentication

et al. 2021

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Behavioural biometrics have the potential to provide an additional or alternative authentication mechanism to those involving a shared secret (i.e. a password). Keystroke timings are the focus of this study, where key press and release timings are acquired whilst monitoring a user typing a known phrase. Many studies exist in keystroke biometrics, but there is an absence of literature aiming to understand the relationship between characteristics of password policies and the potential of keystroke biometrics. Furthermore, benchmark datasets used in keystroke biometric research do not enable useful insights into the relationship between their capability and password policy. Herein, substitutions of uppercase, numeric, special characters, and their combination of passwords derived from English words are considered. Timings for 42 participants for the same 40 passwords are acquired. A matching system using the Manhattan distance measure with seven different feature sets is implemented, culminating in an Equal Error Rate of between 6% and 11% and accuracy values between 89% and 94%, demonstrating comparable accuracy to other threshold‐based systems. Further analysis suggests that the best feature sets are those containing all timings and trigraph press to press. Evidence also suggests that phrases containing fewer characters have greater accuracy, except for those with special character substitutions.

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Section: Related Workmentioning

confidence: 99%

Password policy characteristics and keystroke biometric authentication

et al. 2021

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“…Baynath et al [7] further tested the large-scale applicability of keystroke dynamics, a dataset size for this study was also way larger than the previous ones, as they worked on a combination of the Killourhy Database (CMU database) [3] and their own inbuilt database consisting of fixed text of four different strong passwords. One of the most important conclusions of the study was that the cost of implementation for such system remains low even for large datasets, both computationally and financially.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Free Text Keystroke Authentication Using XGBoost

Kuzminykh,

Mathur,

Ghita

2023

Lecture Notes on Data Engineering and Communications Technologies

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Authentication based on keystroke dynamics is a form of behavioral biometric authentication that uses the user typing patterns and keyboard interaction as a discriminatory input. This type of authentication can be coupled with a fixed text password in a traditional login system to contribute to a multifactor authentication or provide continuous user authentication in a usable security system, where the typing patterns are continuously analysed to validate the user at run time. This paper investigates the effectiveness of free text keystroke for continuous authentication in real-world systems. Evaluation is performed using XGBoost multiclass classification, applied to an unbalanced free-text keystroke dataset. The introduction of additional activity-based features and removal of inaccuracies in the timing between keys allowed a reduction of the EER for the Clarkson II dataset from 14-24%, as achieved by previous studies, to 8% when employing the proposed method.

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“…In [2], a genetic algorithm known as neuro evolution of augmenting topologies (NEAT) is considered. This algorithm achieves a high accuracy on a custom dataset.…”

Section: Previous Workmentioning

confidence: 99%

Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics

Chang¹,

Li²,

Wu³

et al. 2021

Preprint

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Keystroke dynamics can be used to analyze the way that users type by measuring various aspects of keyboard input. Previous work has demonstrated the feasibility of user authentication and identification utilizing keystroke dynamics. In this research, we consider a wide variety of machine learning and deep learning techniques based on fixed-text keystroke-derived features, we optimize the resulting models, and we compare our results to those obtained in related research. We find that models based on extreme gradient boosting (XGBoost) and multi-layer perceptrons (MLP) perform well in our experiments. Our best models outperform previous comparable research.

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Machine Learning Algorithm on Keystroke Dynamics Pattern

Cited by 11 publications

References 16 publications

Password policy characteristics and keystroke biometric authentication

Password policy characteristics and keystroke biometric authentication

Performance Analysis of Free Text Keystroke Authentication Using XGBoost

Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics

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