Modeling Human Tendencies for Password Guessing

Mutalik, Rohit; Chheda, Dhairya; Shaikh, Zeeshan; Toradmalle, Dhanashree

doi:10.1007/978-981-16-5987-4_26

Cited by 1 publication

(1 citation statement)

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“…We chose "RockYou", a corpus of 14 million unique passwords which was available on Kaggle (Mutalik et al, 2021). This corpus contains passwords, leaked from multiple popular applications like 'Facebook', 'myspace' and 'Friendster' etc (Weir et al, 2010).…”

Section: ) Data Sourcementioning

confidence: 99%

Strengthening Passwords Against Peeping Attacks

Jayalath

Delpachithra

Muthunayake

et al. 2022

KDU J. Multidisc. Stud.

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Despite a multitude of vulnerabilities of textual passwords, they are more likely to remain widespread since no scheme has become able to come close to providing all desired benefits. Among those vulnerabilities, peeping attacks are recognized as a real threat but, yet remain much unexplored. Most often, applications and systems use textual passwords for authentication, without considering the threat of peeping attacks. Our study provides the first numerical evidence of strength reduction to represent the impact of the attack. We introduce a novel authentication scheme that is conceptually different but purely text-based, as an endeavour towards strengthening textual passwords against the impact of peeping attacks. An experimental approach was used to collect data, simulating a peeping attack. Researchers intended were to provide an idea to the community, at the level of which the strength of a password can be reduced. This vulnerability is something crucial, yet haven't focused enough. Having such an understanding is desirable, as it can provide an image on the impact that these attacks can have on strength of textual passwords.

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Section: ) Data Sourcementioning

confidence: 99%