MétaCan
Menu
Retour à la cohorte
Enregistrement W980151409

A protection motivation theory approach to improving compliance with password guidelines

2015· dissertation· en· W980151409 sur OpenAlexfundno aff
Florence Mwagwabi

Notice bibliographique

RevueMurdoch Research Repository (Murdoch University) · 2015
Typedissertation
Langueen
DomaineComputer Science
ThématiqueUser Authentication and Security Systems
Établissements canadiensnon disponible
Organismes subventionnairesMcGill University
Mots-clésPasswordPassword strengthComputer securityCognitive passwordComputer scienceCompliance (psychology)Password policyInformation securityInternet privacyVulnerability (computing)One-time passwordPsychologySocial psychology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Usernames and passwords form the most widely used method of user authentication on the Internet. Yet, users still find compliance with password guidelines difficult. The primary objective of this research was to investigate how compliance with password guidelines and password quality can be improved. This study investigated how user perceptions of passwords and security threats affect compliance with password guidelines and explored if altering these perceptions would improve compliance. This research also examined if compliance with password guidelines can be sustained over time. This study focuses on personal security, particularly factors that influence compliance when using personal online accounts. \n \nThe proposed research model is based on the Protection Motivation Theory (PMT) (Rogers, 1975, 1983), a model widely used in information systems security research. As studies have failed to consistently confirm the association between perceived vulnerability and information security practices, the model was extended to include exposure to hacking as a predictor of perceived vulnerability. Experimental research was used to test the model from two groups of Internet users, one of which received PMT based fear appeals in the form of a password security information and training exercise. To examine if password strength was improved by the fear appeals, passwords were collected. A password strength analysis tool was developed using Shannon’s (2001) formula for calculating entropy and coded in Visual Basic. Structural equation modeling was used to test the model. \n \nThe proposed model explains compliance intentions moderately well, with 54% of the variance explained by the treatment model and 43% explained by the control group model. Overall, the results indicate that efficacy perceptions are a stronger predictor of compliance intentions than threat perceptions. This study identifies three variables that predict user intentions to comply with password guidelines as particularly important. These are perceived threat, perceived password effectiveness and password self-efficacy. The results show no association between perceived vulnerability to a security attack and a user’s decision to comply. The results also showed that those who are provided with password information and training are significantly more likely to comply, and create significantly stronger passwords. However, the fear appeals used in this study had no long-term effects on compliance intentions. The results on the long-term effects of password training on the participants’ ability to remember passwords were however promising. The group that received password training with a mnemonic training component was twice as likely to remember their passwords over time. \n \nThe results of this research have practical implications for organizations. They highlight the need to raise the levels of concern for information systems security threats through training in order to improve compliance with security guidelines. Communicating to users what security responses are available is important; however, whether they implement them is dependent on how effective they feel the security responses are in preventing an attack. Regarding passwords, the single most important consideration by a user is whether they have the ability to create strong, memorable passwords. At the very least, users should be trained on how to create strong passwords, with emphasis on memorization strategies. This research found mnemonic password training to have some long-term effects on users’ ability to remember passwords, which is arguably one of the most vexing challenges associated with passwords. Future research should explore the extent to which the effects of PMT based information systems security communication can be maintained over time.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,012
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,043

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,012
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0040,002
Études des sciences et des technologies0,0020,005
Communication savante0,0040,003
Science ouverte0,0020,003
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0070,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,183
Tête enseignante GPT0,341
Écart entre enseignants0,158 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2015
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueMurdoch Research Repository (Murdoch University)Même sujetUser Authentication and Security SystemsTravaux en français237 207