The physiology of keystroke dynamics

Jenkins, Jeffrey L.; Nguyen, Quang H.; Reynolds, Joseph P.; Horner, William C.; Szu, Harold

doi:10.1117/12.887419

Cited by 3 publications

(3 citation statements)

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“…ANN USL algorithm is approximately 0. We interpret this convergence as a habituation memory mechanism biomarker that has been detected by a few of the authors in other work [16]. Due to the striking visual representation of 'un-mined' data, we push forward with sophisticated machine learning tools to elucidate the invariant soft-biometric features within DTAB data.…”

Section: -Non-traditional Bio-feedback Data Mining Based On Physiologysupporting

confidence: 52%

“…We sought after 5 Fig. 5 -Change in Kurtosis for sequentially learned PIN numbers on standard keyboard -(a) User 1 typed PIN number with one hand every trial on pin pad, (b) User 2 typed PIN number with two hands every trial on horizontal number bar [16]. We wish to study the EEG signal concurrently with PGIN HOS.…”

Section: -Non-traditional Bio-feedback Data Mining Based On Physiologymentioning

confidence: 99%

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Authentication, privacy, security can exploit brainwave by biomarker

Jenkins

Sweet

Sweet³

et al. 2014

SPIE Proceedings

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We seek to augment the current Common Access Control (CAC) card and Personal Identification Number (PIN) verification systems with an additional layer of classified access biometrics. Among proven devices such as fingerprint readers and cameras that can sense the human eye's iris pattern, we introduced a number of users to a sequence of 'grandmother images', or emotionally evoked stimuliresponse images from other users, as well as one of their own, for the purpose of authentication. We performed testing and evaluation of the Authenticity Privacy and Security (APS) brainwave biometrics, similar to the internal organ of the human eye's iris which cannot easily be altered. 'Aha' recognition through stimulus-response habituation can serve as a biomarker, similar to keystroke dynamics analysis for inter and intra key fluctuation time of a memorized PIN number (FIST). Using a non-tethered Electroencephalogram (EEG) wireless smartphone/pc monitor interface, we explore the appropriate stimuli-response biomarker present in DTAB low frequency group waves. Prior to login, the user is shown a series of images on a computer display. They have been primed to click their mouse when the image is presented. DTAB waves are collected with a wireless EEG and are sent via Smartphone to a cloud based processing infrastructure. There, we measure fluctuations in DTAB waves from a wireless, non-tethered, single node EEG device between the Personal Graphic Image Number (PGIN) stimulus image and the response time from an individual's mental performance baseline. We hope Higher Order Statistics (HOS) in typing and PGIN can reveal intrinsic personal traits/habits and increase EEG specificity through stimulus-response neuro-biomarkers. Towards that goal, we describe an infrastructure that supports distributed verification for webbased EEG authentication. The performance of machine learning on the relative Power Spectral Density EEG data may uncover features required for subsequent access to web or media content. Our approach provides a scalable framework wrapped into a robust Neuro-Informatics toolkit, viable for use in the Biomedical and mental health communities, as well as numerous consumer applications.

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Section: -Non-traditional Bio-feedback Data Mining Based On Physiologysupporting

confidence: 52%

Section: -Non-traditional Bio-feedback Data Mining Based On Physiologymentioning

confidence: 99%

Authentication, privacy, security can exploit brainwave by biomarker

Jenkins

Sweet

Sweet³

et al. 2014

SPIE Proceedings

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“…In the following equations, as the resulting percent deviations approach ε ≈ .7, we can plot changes based on the telescopic viewpoint to aid in the quantitative analysis of microscopic measurement and how it's changes alter the macroscopic picture. Following the equations outlined below, one can represent a nano-scale telescopic imaging system mathematically [11][15]: We define a uniform weighted batched average of our data set to be the angular brackets…”

Section: -Real and Synthetic Multi-resolution Imaging And Analysis Enmentioning

confidence: 99%

Telescopic multi-resolution augmented reality

2014

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To ensure a self-consistent scaling approximation, the underlying microscopic fluctuation components can naturally influence macroscopic means, which may give rise to emergent observable phenomena. In this paper, we describe a consistent macroscopic (cm-scale), mesoscopic (micron-scale), and microscopic (nano-scale) approach to introduce Telescopic Multi-Resolution (TMR) into current Augmented Reality (AR) visualization technology. We propose to couple TMR-AR by introducing an energy-matter interaction engine framework that is based on known Physics, Biology, Chemistry principles. An immediate payoff of TMR-AR is a self-consistent approximation of the interaction between microscopic observables and their direct effect on the macroscopic system that is driven by real-world measurements. Such an interdisciplinary approach enables us to achieve more than multiple scale, telescopic visualization of real and virtual information but also conducting thought experiments through AR. As a result of the consistency, this framework allows us to explore a large dimensionality parameter space of measured and unmeasured regions. Towards this direction, we explore how to build learnable libraries of biological, physical, and chemical mechanisms. Fusing analytical sensors with TMR-AR libraries provides a robust framework to optimize testing and evaluation through data-driven or virtual synthetic simulations. Visualizing mechanisms of interactions requires identification of observable image features that can indicate the presence of information in multiple spatial and temporal scales of analog data. The AR methodology was originally developed to enhance pilot-training as well as 'make believe' entertainment industries in a user-friendly digital environment We believe TMR-AR can someday help us conduct thought experiments scientifically, to be pedagogically visualized in a zoom-in-and-out, consistent, multi-scale approximations.

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Improving Mobile Device Security by Embodying and Co-adapting a Behavioral Biometric Interface

Jairam

Halevi

Raphan

2022

Front. Comput. Sci.

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At present, interfaces between users and smart devices such as smart phones rely primarily on passwords. This has allowed for the intrusion and perturbation of the interface between the user and the device and has compromised security. Recently, Frank et al. have suggested that security could be improved by having an interface with biometric features of finger swiping. This approach has been termed touchalytics, in maintaining cybersecurity. The number of features of finger swiping have been large (32) and have been made available as a public database, which we utilize in our study. However, it has not been shown which of these features uniquely identify a particular user. In this paper, we study whether a subset of features that embody human cognitive motor features can be used to identify a particular user. We consider how the security might be made more efficient embodying Principal Component Analysis (PCA) into the interface, which has the potential of reducing the features utilized in the identification of intruders. We compare the accuracy and performance of the reduced feature space to that of having all the features. Embodying a robust continuous authentication system will give users an extra layer of security and an increased sense of peace of mind if their devices are lost or stolen. Consequently, such improvements may prevent access to sensitive information and thus will save businesses money. Consequently, such improvements may prevent access to sensitive information and thus will save businesses money. If continuous authentication models become successful and easily implementable, embodiment and co-adaptation of user authentication would inhibit the growing problem of mobile device theft.

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The physiology of keystroke dynamics

Cited by 3 publications

References 3 publications

Authentication, privacy, security can exploit brainwave by biomarker

Authentication, privacy, security can exploit brainwave by biomarker

Telescopic multi-resolution augmented reality

Improving Mobile Device Security by Embodying and Co-adapting a Behavioral Biometric Interface

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