Grant: Discovery Grants Program - Individual
University of Ontario Institute of Technology
$28,000.00
Enhancing Authentication: Towards Password Memorability Meters, and Leveraging Implicit Learning for System-Assigned Passwords
EF 2019-20
Résumé
Text passwords remain as one of the most widespread authentication mechanisms in computer systems. The strength of a password is thus of paramount importance to security. As such, a number of password strengthening techniques have been proposed in the literature and deployed in the real world. Nevertheless, there exists a trade-off between password strength and memorability, and while password strength meters are common, according to the related literature and to the best of my knowledge, no reliable memorability meter has been published or deployed. The proposed research program will endeavour to analyze memorability of system-assigned passwords at the time of creation, by studying the brain waves generated while seeing a password for the first time. This work will lead to a password meter that can help users to choose a system-assigned password that has higher probability of being remembered, by predicting its recall chances at the time of assigning the password to a user.******So far, together with my team of students, I have been able to establish that passwords can be classified based on the electroencephalogram (EEG) signals they elicit. This was found through a series of experiments that used consumer-grade brain-computer interface (BCI) devices to compare EEG differences between random and common passwords (i.e., most common passwords as exposed by popular password leaks). In light of these experiments it became evident that a password meter could be built by measuring short- and long-term password recall. For example, when the user is given a choice of two system-assigned passwords, there are statistically significant correlations between the EEG signals and the chosen password, recalling the password from short- and long-term memory, and the number of attempts to remember the password correctly.******To address password usability from a different angle, the proposed research program will investigate authentication techniques that leverage implicit learning phenomena from the psychology field. Some of the most important advantages of implicitly learnt passwords are that the user is not imposed with a conscious cognitive burden, can't retrieve them at will, and therefore to a certain extent implicitly learnt passwords are coercion- and insider-attack resistant. During the past years, my research team and I have designed and tested an authentication technique based on two branches of implicit learning, namely, contextual cueing and semantic priming, which have shown promise not only in terms of accuracy but in training time compared to the most cited recent related works available in the literature. The proposed research program will continue this line of research to improve accuracy as well as training and authentication time.******Over 10 highly qualified personnel will be trained in authentication with strong theoretical foundations, benefitting the research community and Canadians in general.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
RGPIN-2018-05919
Name nom
VargasMartin, Miguel
Department département
Business and Information Technology, Faculty of
Organization id
19222
Institution établissement
University of Ontario Institute of Technology
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2019
Competition year année de concours
2018
Award amount
28000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1507
Committee name en
Computer Science
Committee name fr
Sciences informatiques
Area of application code
1207
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Information, computer and communication technologies
Area of application fr
Technologie de l'information, des ordinateurs et des réseaux de communications
Research subject code
2710
Research subject group en
Information technology
Research subject group fr
Technologie de l'information
Research subject en
Information systems design
Research subject fr
Conception de systèmes d'information
Installment
inst:2 - 5
Partie
2
Nb partie
5
Application title
Enhancing Authentication: Towards Password Memorability Meters, and Leveraging Implicit Learning for System-Assigned Passwords
Keyword
NSERC
Application summary
Text passwords remain as one of the most widespread authentication mechanisms in computer systems. The strength of a password is thus of paramount importance to security. As such, a number of password strengthening techniques have been proposed in the literature and deployed in the real world. Nevertheless, there exists a trade-off between password strength and memorability, and while password strength meters are common, according to the related literature and to the best of my knowledge, no reliable memorability meter has been published or deployed. The proposed research program will endeavour to analyze memorability of system-assigned passwords at the time of creation, by studying the brain waves generated while seeing a password for the first time. This work will lead to a password meter that can help users to choose a system-assigned password that has higher probability of being remembered, by predicting its recall chances at the time of assigning the password to a user.******So far, together with my team of students, I have been able to establish that passwords can be classified based on the electroencephalogram (EEG) signals they elicit. This was found through a series of experiments that used consumer-grade brain-computer interface (BCI) devices to compare EEG differences between random and common passwords (i.e., most common passwords as exposed by popular password leaks). In light of these experiments it became evident that a password meter could be built by measuring short- and long-term password recall. For example, when the user is given a choice of two system-assigned passwords, there are statistically significant correlations between the EEG signals and the chosen password, recalling the password from short- and long-term memory, and the number of attempts to remember the password correctly.******To address password usability from a different angle, the proposed research program will investigate authentication techniques that leverage implicit learning phenomena from the psychology field. Some of the most important advantages of implicitly learnt passwords are that the user is not imposed with a conscious cognitive burden, can't retrieve them at will, and therefore to a certain extent implicitly learnt passwords are coercion- and insider-attack resistant. During the past years, my research team and I have designed and tested an authentication technique based on two branches of implicit learning, namely, contextual cueing and semantic priming, which have shown promise not only in terms of accuracy but in training time compared to the most cited recent related works available in the literature. The proposed research program will continue this line of research to improve accuracy as well as training and authentication time.******Over 10 highly qualified personnel will be trained in authentication with strong theoretical foundations, benefitting the research community and Canadians in general.