A Novel Approach To MultiFactor Authentication Through Sensorimotor Coupled Implicit Learning
A. Chhetri, Dr. J. Silvanto, Department of Psychology, Faculty of Science & Technology, University of Westminster
Fully harnessing the nature of implicit memory has been long sought after by researchers due to its practical applications within education, cognitive neuroscience, and psychology. To add to the body of research, this paper shall extend the interest of tacit learning to the field of computer science where security is concerned. The research offers an efficient solution to the rubber-hose cryptanalysis problem by manipulating implicit (procedural) memory through training participants a high-entropy underlying target sequence in a visuomotor typing task. On average, the target sequence reaction time was found to be significantly lower in the trained group when compared to the untrained group. Target sequence performance advantage was also found to increase from session 1 to session 2 by 52.3% in the trained group. While in the untrained group, Target sequence performance advantage decreased across sessions by 27.8%. But most important of all, participants failed to recall overall sequence structure, establishing tacit skill expression outside of explicit awareness. Therefore, ensuring that an authentication protocol is built around what users implicitly learn through prior sensorimotor training. Thus guaranteeing that critical information is not susceptible to coercion. The findings hope to lay a framework for future crypto primitives in large-scale security protocols.