Dataset Information

Facelock: familiarity-based graphical authentication.

ABSTRACT: Authentication codes such as passwords and PIN numbers are widely used to control access to resources. One major drawback of these codes is that they are difficult to remember. Account holders are often faced with a choice between forgetting a code, which can be inconvenient, or writing it down, which compromises security. In two studies, we test a new knowledge-based authentication method that does not impose memory load on the user. Psychological research on face recognition has revealed an important distinction between familiar and unfamiliar face perception: When a face is familiar to the observer, it can be identified across a wide range of images. However, when the face is unfamiliar, generalisation across images is poor. This contrast can be used as the basis for a personalised 'facelock', in which authentication succeeds or fails based on image-invariant recognition of faces that are familiar to the account holder. In Study 1, account holders authenticated easily by detecting familiar targets among other faces (97.5% success rate), even after a one-year delay (86.1% success rate). Zero-acquaintance attackers were reduced to guessing (<1% success rate). Even personal attackers who knew the account holder well were rarely able to authenticate (6.6% success rate). In Study 2, we found that shoulder-surfing attacks by strangers could be defeated by presenting different photos of the same target faces in observed and attacked grids (1.9% success rate). Our findings suggest that the contrast between familiar and unfamiliar face recognition may be useful for developers of graphical authentication systems.

SUBMITTER: Jenkins R

PROVIDER: S-EPMC4081289 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Publications

Facelock: familiarity-based graphical authentication.

Jenkins Rob R McLachlan Jane L JL Renaud Karen K

PeerJ 20140624

Authentication codes such as passwords and PIN numbers are widely used to control access to resources. One major drawback of these codes is that they are difficult to remember. Account holders are often faced with a choice between forgetting a code, which can be inconvenient, or writing it down, which compromises security. In two studies, we test a new knowledge-based authentication method that does not impose memory load on the user. Psychological research on face recognition has revealed an im ...[more]

PMID: 25024913

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Project description:Failed user authentication is a common event. Forgotten passwords and fingerprint non-recognition are the most common causes. Therefore, there is a need for efficient backup authentication methods, known as fallback authentication. However, fallback authentication methods suffer from different security and usability issues. This study aims to improve the security and usability of knowledge-based fallback authentication in the form of static security questions. The approach proposed in this study was designed considering different factors, such as question features, authentication mechanisms, and the use of tools to aid in composing memorable and secure answers. This study used a two-part experiment with 23 participants to evaluate the proposed approach based on security model testing. The results show that the proposed approach offered improved resistance to blind guess, focused guess, and observation guess attacks. While usability was clearly improved with questions that were based on recognition mechanisms, our results indicate that fallback authentication systems need a flexible level of security and avoidance of complexity in composing answers. In addition, our results indicate the effectiveness of using user behavioral details in the choice of topics for questions, where behavioral questions must have both high recall levels and resistance against guessing attacks. This work theoretically extends the knowledge of fallback authentication research by evaluating new security questions for fallback authentication considering replace of classical topics of security questions by introducing new topics of security questions based on user behavior and personal preferences. Also, this study applies methods of managing answers to security questions by encouraging users to compose answers based on free strict rules that inspire them to create strong and memorable answers based on their own rules. In addition, the findings of this study could support the deployment of knowledge-based authentication in fallback systems as a practical contribution to the user authentication field.

Dataset Information

Facelock: familiarity-based graphical authentication.

Publications

Facelock: familiarity-based graphical authentication.

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