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Publication Type : Conference Paper
Thematic Areas : Biotech, Learning-Technologies, Medical Sciences
Publisher : 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), IEEE, Chennai, India .
Source : 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), IEEE, Chennai, India (2016)
Url : https://scholar.google.com/scholar?oi=bibs&cluster=383992616082967746&btnI=1&hl=en
Keywords : Alpha-numeric password-based login, Authentication, authorisation, classification, Classification algorithms, cognition, cognitive performance evaluation, computer assisted technology, computer network security, EEG, EEG recording, EEG-based assessment, Electroencephalography, human user authentication, Image sequence, image sequence training, image sequence-based user authentication, image sequences, medical signal processing, memory performance evaluation, passkey-based authentication mechanism, passwords, personalized authentication, Rubber-Hose Attack, security system, Sequence learning, Support vector machines, textual password, Training, User authentication, α rhythm activity, β rhythm .
Campus : Amritapuri
School : Centre for Cybersecurity Systems and Networks, School of Biotechnology
Center : Amrita Mind Brain Center, Biotechnology, Computational Neuroscience and Neurophysiology, Cyber Security
Department : biotechnology, cyber Security
Year : 2016
Abstract : User authentication is crucial in security systems. Although, there are many complex and secure passkey-based authentication mechanisms, majority of users prefer employing simple passwords that are viable to rubber-hose attacks. Image sequence based passwords were introduced to overcome some of the issues with textual passwords. The objective of this work was to evaluate cognitive and memory performance in image-based user authentication systems. Via EEG recordings during image sequence training task, we observed increased activity of α rhythms in F3 and FC5 channel bins and augmented levels of β rhythms in F3 and O1 channels, suggesting users personalized authentication more than in alpha-numeric password-based logins, linking potential roles of implicit and explicit sequence learning in improving human user authentication.
Cite this Research Publication : P. Chellaiah, Sandeep Bodda, Rahul Lal, Clinton Madhu, Vaibhav Zamare, Dr. Bipin G. Nair, Dr. Shyam Diwakar, and Dr. Krishnashree Achuthan, “EEG-based assessment of image sequence-based user authentication in computer network security”, in Proceedings of IEEE International Conference on Electrical, Electronics and Optimization Techniques (ICEEOT 2016), Chennai, March 3-5, 2016.