Keystroke Dynamics Based User Authentication using Deep Multilayer Perceptron

Andrean, Alvin; Jayabalan, Manoj; Thiruchelvam, Vinesh

doi:10.18178/ijmlc.2020.10.1.910

Cited by 17 publications

(7 citation statements)

References 10 publications

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“…Subsequent studies were able to improve in terms of accuracy. For example, one study [18] also applied MLPs on 20,400 data samples using 34 features and achieved an Equal Error Rate ( eer ) of 4.45%. The dataset was acquired from 51 subjects by typing .tie5Roanl as the password, which contains a special character, upper and lowercase characters as well as a number.…”

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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“…al. [23] proposed a deep learning technique using Multilayer Perceptron (MLP). Their MLP has 1 input layer and 2 hidden layers and 1 output layer.…”

Section: Related Workmentioning

confidence: 99%

Keystroke dynamics based user authentication using Histogram Gradient Boosting

Ibrahim

AbdelRaouf²,

Amin³

et al. 2022

IJCI. International Journal of Computers and Information

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User authentication is a vital part of securing digital services and preventing unauthorized users from gaining access to the system. Nowadays, organizations use Multi-Factor Authentication (MFA) to provide robust protection by utilizing two or more identity procedures instead of using Single Factor Authentication (SFA) which became less secure. Keystroke dynamics is a behavioral biometric that examines a user's typing rhythm to determine the subject's legitimacy using the system. Keystroke dynamics have a minimal implementation cost and do not need special hardware in the authentication process since the gathering of typing data is reasonably straightforward and does not involve additional effort from the user. In this work, we present an efficient approach that uses the quantile transformation that transforms data distribution into uniform distribution which significantly reduces the impact of outlier and extreme values. Histogram Gradient Boosting is employed as the primary classifier for the training and testing phase. Our proposed approach is evaluated on Carnegie Mellon University (CMU) keystroke benchmark dataset which has achieved 97.96% of average accuracy and 0.014 of average equal error rate (EER) across all subjects which outperforms all the previous advances in both machine and deep learning approaches.

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“…In KD literature, the common metric is EER. Several recent studies [137], [160], [178], [194], [321]- [323] used this metric to measure the performance of their proposed model. A study [324] used both EER and accuracy to measure the performance of a distance-based detector.…”

Section: ) Performance Evaluation Metrics For Data Acquisitionmentioning

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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Keystroke Dynamics Based User Authentication using Deep Multilayer Perceptron

Cited by 17 publications

References 10 publications

Password policy characteristics and keystroke biometric authentication

Password policy characteristics and keystroke biometric authentication

Keystroke dynamics based user authentication using Histogram Gradient Boosting

A Systematic Literature Review on Latest Keystroke Dynamics Based Models

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