Filtering honeywords using probabilistic context free grammar

dc.contributor.advisorVargas Martin, Miguel
dc.contributor.authorTanniru, Alekhya
dc.date.accessioned2024-02-27T21:15:06Z
dc.date.available2024-02-27T21:15:06Z
dc.date.issued2023-10-01
dc.degree.disciplineArtificial Intelligenceen
dc.degree.levelMaster of Information Technology Security (MITS)en
dc.description.abstractWith the growing prevalence of cyber threats, effective password policies have become crucial for safeguarding sensitive information. Traditional password-based authentication techniques are open to a number of threats. The idea of honeywords, which was developed to improve password-based security, entails using dummy passwords with real ones to build a defence mechanism based on deceit. The importance of password policies is examined in the context of honeywords in this study, emphasizing how they might improve security and reduce password-related risks. We present the idea of using the existing passwords to extract a policy and using this policy to filter good and strong passwords. Through this capstone project, we aim to contribute to the broader understanding of honeywords and their role in improving password-based authentication systems. I have conducted experiments on Chunk-GPT3 and GPT 4 models, to see which one of the models produces more honeywords which are very similar to the real passwords.en
dc.description.sponsorshipUniversity of Ontario Institute of Technologyen
dc.identifier.urihttps://hdl.handle.net/10155/1760
dc.language.isoenen
dc.subjectPasswordsen
dc.subjectHoneywordsen
dc.subjectPCFGen
dc.subjectGPT modelsen
dc.titleFiltering honeywords using probabilistic context free grammaren
dc.typeMaster's Projecten

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