EEG-based assessment of image sequence-based user authentication in computer network security

Chellaiah, Priya; Bodda, Sandeep; Lal, Rahul; Madhu, C.; Zamare, Vaibhav; Nair, Bipin; Diwakar, Shyam; Achuthan, Krishnashree

doi:10.1109/iceeot.2016.7755395

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…From this analysis we may conclude that the employment of AI algorithms contributes significantly to the observed high-accuracy rates for the EEG-based User Authentication task when utilizing Mental-Activity Protocols (Figure 8). However, in some cases researchers have reported lower accuracy [11,16] which nonetheless might also be accredited to the EEG-device setup characteristics.…”

Section: Mental Activity Protocolsmentioning

confidence: 99%

A Review of EEG-Based User Authentication: Trends and Future Research Directions

Fidas

Lyras²

2023

IEEE Access

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The employment of Electroencephalography (EEG) in the User Authentication (UA) scientific research has recently unlocked state-of-the-art experimentation, aiming at identifying and authenticating individuals given their brainiac activity within specific contexts of use. Indeed, utilizing EEG signals that are derived from brainiac activities can be used for tackling existing UA security threats such as shoulder surfing, thus providing a novel solution to contemporary security problems in traditional knowledge-based user authentication. In this survey, we aim to complete previous literature surveys by providing a systematic classification and presentation of existing research that is based on the following pillars: a) the user experimental setup, with an emphasis on the applied EEG-acquisition protocols (e.g., rest, external stimuli driven, mental and hybrid); b) the artificial intelligence techniques employed and finally c) the security and privacy preservation aspects. The reviewed papers cover a broad area of experiment protocols and various algorithms used for EEG signal classification. Moreover, most cited works include results from more than one experiments with different approaches and configurations. This leads to a discussion on best practices for EEG-based User Authentication and conclusions suggesting future research directions that consists, among others, of considering homomorphically encrypted biometric templates for information leakage prevention through federated learning approaches in decentralized architectures. We anticipate that the present literature review will provide a roadmap for future research by considering efficiently and effective EEG-based User Authentication methods while at the same time preserving privacy.

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Section: Mental Activity Protocolsmentioning

confidence: 99%

A Review of EEG-Based User Authentication: Trends and Future Research Directions

Fidas

Lyras²

2023

IEEE Access

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“…As a preliminary assessment [24], to evaluate user brain activity behavior behind the proposed system, EEG data was collected from 4 healthy volunteers of mean age 23 after an informed consent and prior ethical clearance from institutional committee. A commercial EEG headset [25] with14+2 channel electrodes was employed and experiments were conducted in a noise free environment.…”

Section: Narrative Construction Based Taskmentioning

confidence: 99%

Using Theme-based Narrative Construct of Images as Passwords: Implementation and Assessment of Remembered Sequences

et al. 2017

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Abstract-With many online engineering platforms such as virtual and remote laboratories designed for young or aged users, user authentication and passwords-based methods are being re-evaluated for tracking usage patterns and security. For ICT-enabled online engineering platforms, image-based humancentric approaches are gaining relevance for access frameworks. With the rubber-hose attacks, increased senior users, many existing systems are vulnerable to many attacks. This paper employs human uniqueness of narrative skills on an image-based password system for online platforms with focus on theme in the password generation process. To generate the secret password, a specially designed computer game was used. We used narrative constructs composed of cartoon image sequences to generate user-speci!c secret key. The durability of generated passwords and the authentication process while assessing the reconstruction process by a potential hacker was verified. For validating use of coerced attacks, under imposed psychological duress, users failed retrieving the password sequence suggesting the reliability as an anti-coercive attack cybersecurity tool. A set of experiments were used to analyze user behavior behind the image-based password system. EEG measurements demonstrated increased activity of " rhythms in F3 and FC5 channel bins and augmented levels of # rhythms in F3 and O1 channels, suggesting users added personalization to authentication more than in alpha-numeric password-based logins.

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EEG based Control using Spectral Features

Poorna¹,

Anuraj²,

Renjith

et al. 2018

2018 2nd International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)i-Smac (IoT in Social, Mobile,

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EEG-based assessment of image sequence-based user authentication in computer network security

Cited by 6 publications

References 17 publications

A Review of EEG-Based User Authentication: Trends and Future Research Directions

A Review of EEG-Based User Authentication: Trends and Future Research Directions

Using Theme-based Narrative Construct of Images as Passwords: Implementation and Assessment of Remembered Sequences

EEG based Control using Spectral Features

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