Use this url to cite publication: https://cris.mruni.eu/cris/handle/007/18553
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Markov, Probabilistic and Rule-Based Password Guessing Methods: Survey and Comparison
Type of publication
Straipsnis kitame recenzuojamame leidinyje / Article in other peer-reviewed edition (S5)
Type of publication (old)
S4
Author(s)
Chaževskas, Andrius |
Belovas, Igoris |
Marcinkevičius, Virginijus |
Title
Markov, Probabilistic and Rule-Based Password Guessing Methods: Survey and Comparison
Other Title
Markovo tikimybėmis ir taisyklėmis pagrįstų slaptažodžių parinkimo metodų apžvalga ir palyginimas
Date Issued
2022
Is part of
Kriminalistika ir teismo ekspertologija: mokslas, studijos, praktika XVIII. ISSN 2783-7068, 2022, T. 18
Field of Science
Abstract
Offline password guessing is an important procedure for
forensic encrypted data examination where the data must be
decrypted first. The most common password guessing attacks
are dictionary and brute-force, but the main drawback of a
brute-force attack is the size of a set of all possible password
candidates, which grows exponentially with the length of the
password. The analysis of leaked password databases shows
that users tend to use easy-to-remember passwords. It means
that many passwords usually consist of a logical structure -
they are not just random character sets. Forensic information technology experts could exploit this defect using different
offline password guessing strategies relying on new password
generation rules, machine learning, and natural language
processing. This research offers a survey and comparison of
the state-of-the-art password guessing methods such as Rulebased,
Markov, Probabilistic Context-Free Grammar which
can be applied in forensic IT examinations.
Type of document
type::text::journal::journal article::research article
Language
Anglų / English (en)
Access Rights
Atviroji prieiga / Open Access