What Your Brain Says About Your Password: Using Brain-Computer Interfaces to Predict Password Memorability

Alomari, Ruba; Martin, Miguel Vargas; MacDonald, Shane; Bellman, Christopher; Liscano, Ramiro; Maraj, Amit

doi:10.1109/pst.2017.00024

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…Our experiment verified the existence of a correlation between the EEG signal and the corresponding hand movement signal in two specific scenarios: (1) watching a video with emotional content and (2) typing about the video. Other scenarios such as password typing [3] and rest state [37] need to be analyzed. (3) Other EEG-based authentication methods that use different verification approaches need to be analyzed for the existence of correlation and the effect of the correlation of EEG signals with other easily recordable signals.…”

Section: Discussionmentioning

confidence: 99%

“…2 An adversary can steal a targeted user's hand movements by sending or installing a malicious application on the user's smartwatch device (see Liu and Sun [26]). 3 The knowledge of correlation can be obtained using publicly available datasets or from articles such as ours. Thinking Unveiled: An Inference and Correlation Model to Attack EEG Biometrics • 9:5 attack performance results are presented in Section 6.…”

Section: Introductionmentioning

confidence: 99%

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Thinking Unveiled

et al. 2020

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Very few studies have explored linkages between physiological, such as electroencephalograph (EEG), and behavioral patterns, such as wrist movements. These linkages provide us a unique mechanism to predict one set of patterns from other related patterns. Unlike conventional biometrics, EEG biometrics are hard to spoof using standard presentation attack methods, given the intrinsic liveness resulting from the bounded randomness of EEG signals specific to an individual. In this article, we propose a novel attack on the EEG-based authentication systems by investigating and leveraging the strong correlation between hand movements and brain signals captured through the motion sensors on a smartwatch and the wearable EEG headset, respectively. Based on this technique, we can successfully estimate the user's EEG signals from the stolen hand movement data while the user was typing on the keyboard. Our attack results on the EEG biometric authentication system show an increase in the mean equal error rates of the classifiers by between 180% and 360% based on a dataset of 59 users. In summary, our pilot study calls for a rethinking of EEG-based authentication mechanisms from the perspective of unique vulnerabilities, particularly for multimodal biometric systems involving a variety of wearable or mobile devices.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Thinking Unveiled

et al. 2020

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“…Narayana et al (2019) present an important application for the physically challenged consisting of a biometric security system configured by a BCI to lock/unlock a wheelchair and control its movements using these patterns that occur due to eye blinks and activity of muscles in the jaw. Alomari et al (2017) propose that a practical EEG-based system could be developed to make it easier for users to select a password based on the prediction of its memorization at the time of its creation. Merrill (2019) designed a brain-based authentication system using custom-fit EEG earpieces.…”

Section: Biometric Authenticationmentioning

confidence: 99%

Brain-computer interfaces in safety and security fields: Risks and applications

Brocal

2023

Safety Science

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“…This may lead to an increase in password disclosure, thereby causing a lot of users to avoid such restrictions to produce passwords easier to memorize. Although these findings are almost two decades old, from the security awareness perspective, users (mostly non-technical) still make similar mistakes [30][31][32], and often need an additional level of warnings or nudges [33].…”

Section: Password Habits and Common Practicesmentioning

confidence: 99%

Is My Password Strong Enough?: A Study on User Perception in The Developing World

Tanni¹,

Taharat²,

Parvez³

et al. 2022

EAI Endorsed Transactions on Creative Technologies

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INTRODUCTION:The first line of defense in the cyber world is strong and difficult to predict passwords. However, users often choose highly predictable passwords based on personal information, dictionary words, birth date, etc. OBJECTIVES: The primary objective is to ascertain password choice and practices of users of developing countries. METHODS: Most of the existing studies are done in the developed world and our exhaustive search failed to find similar research in the context of developing countries. Here, we conducted detailed surveybased scrutiny about the passwordbased security perceptions of Bangladeshi nationals, which include 881 participants, primarily students, and professionals. RESULTS: Most of the users were found to have bad practices, for example, having personal information(56%), password reuse(69%), having commonly used patterns(81.3%). Students from technical backgrounds fared well compared to non-technical backgrounds as expected. However, some professionals (especially Bankers) surprisingly chose weaker passwords even though dealing with sensitive data. CONCLUSION: We also make a few recommendations to improve awareness.

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What Your Brain Says About Your Password: Using Brain-Computer Interfaces to Predict Password Memorability

Cited by 7 publications

References 21 publications

Thinking Unveiled

Thinking Unveiled

Brain-computer interfaces in safety and security fields: Risks and applications

Is My Password Strong Enough?: A Study on User Perception in The Developing World

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