Utilizing overt and latent linguistic structure to improve keystroke-based authentication

Goodkind, Adam; Brizan, David Guy; Rosenberg, Andrew

doi:10.1016/j.imavis.2016.06.003

Cited by 11 publications

(5 citation statements)

References 32 publications

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“…Table 2 gives a summary of comparison of performances (in terms of EER) of some of the best methods used in the most recent literature for keystroke dynamics. Out of which, a system designed by Goodkind et al (2017) demonstrates the importance of context when authenticating a typist. According to the authors, keystroke production is influenced by a number of factors including linguistic context and structure.…”

Section: Results and Performance Validationmentioning

confidence: 99%

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An authentication system using keystroke dynamics

Zareen

Matta

Arora

et al. 2018

IJBM

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There are various biometrics-based methods for user authentication. However, the best authentication method can be based on physiological/behavioural biometrics as capturing physiological biometrics may require use of special devices and that may not be available with many users. Keystroke dynamics is a simplified and easily achievable user authentication method when every user is available with a laptop or a personal computer. This paper presents a keystroke dynamics-based authentication system using Bayesian regularised feed-forward neural network. In order to train the model, a database is captured for recording keystroke dynamics of 20 users in four sessions each with 50 samples. Experimental results demonstrate that the Bayesian regularised neural network models provide the best results and are most suitable for this purpose. We are able to achieve an equal error rate of 0.9% which is better than the methods used in the existing literature for plain keystroke dynamics. We have given a comparative analysis of the performance of proposed system with existing methods.

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Section: Results and Performance Validationmentioning

confidence: 99%

“…Meanwhile, AR-ONENB sorts the attributes based on the time length during the preprocessing phase for effective classification. Goodkind et al (2017) have added the linguistic context under which keystrokes are produced.…”

Section: Related Work and Benchmark Keystroke Databasesmentioning

confidence: 99%

An authentication system using keystroke dynamics

Zareen

Matta

Arora

et al. 2018

IJBM

View full text Add to dashboard Cite

show abstract

“…Generally, the probabilities of the corresponding false events are estimated: a false denial of access for a registered user and a false provision of access for an unregistered user. By analogy with estimates in statistical radio engineering, the following errors are most commonly used in keystroke dynamics studies [ 17 , 20 , 21 , 36 , 38 ].…”

Section: Keystroke Authentication and Identificationmentioning

confidence: 99%

Hidden Monitoring Based on Keystroke Dynamics in Online Examination System

Kochegurova

Zateev

2022

Program Comput Soft

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The COVID-19 pandemic has accelerated the development of distance learning technologies, where online tests and exams play an important role. In online testing, the detection of various types of academic fraud, including cases where the examinee is falsely represented by another person, is of particular importance. Continuous biometric (behavioral) authentication can be a solution to counter unauthorized access. This paper proposes an authentication technology based on keystroke dynamics and hidden monitoring. An application for collecting and updating keystroke dynamics profiles of domain users and their continuous authentication is developed. The efficiency of reducing the dimension of the keystroke feature space based on the frequency of alphabetic letters is demonstrated. Results of popular performance metrics (FAR, FRR, ERR, ROC, and DET) are significantly improved already when evaluating only metric distances. For instance, ERR is reduced from 10.1% to 0.79%, which is comparable to the estimates provided by the kNN method for its optimal parameters.

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“…6. The keystroke data contains the information about user authentication data, especially for keystroke authentications [4,40,64,135]. Leveraging the acceleration, acoustic and video information, we review the attacks stealing these keystroke information and the countermeasures.…”

Section: 31mentioning

confidence: 99%

Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey

Miao,

Chen,

Pan

et al. 2021

Preprint

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Stealing attack against controlled information, along with the increasing number of information leakage incidents, has become an emerging cyber security threat in recent years. Due to the booming development and deployment of advanced analytics solutions, novel stealing attacks utilize machine learning (ML) algorithms to achieve high success rate and cause a lot of damage. Detecting and defending against such attacks is challenging and urgent so that governments, organizations, and individuals should attach great importance to the ML-based stealing attacks. This survey presents the recent advances in this new type of attack and corresponding countermeasures. The ML-based stealing attack is reviewed in perspectives of three categories of targeted controlled information, including controlled user activities, controlled ML model-related information, and controlled authentication information. Recent publications are summarized to generalize an overarching attack methodology and to derive the limitations and future directions of ML-based stealing attacks. Furthermore, countermeasures are proposed towards developing effective protections from three aspects-detection, disruption, and isolation.

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Utilizing overt and latent linguistic structure to improve keystroke-based authentication

Cited by 11 publications

References 32 publications

An authentication system using keystroke dynamics

An authentication system using keystroke dynamics

Hidden Monitoring Based on Keystroke Dynamics in Online Examination System

Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey

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