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.
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 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, 2016.