WoG is a python tool designed to generate wordlists by applying some transformations to a small set of input words. How does it work? WoG starts by reading few words from an input file, then it applies to each word read an additivite trasformation, in order to further expand the starting word set. When the expansion of the initial set has been completed, WoG starts combininig each word with the others. If some separators have been specified, WoG repeats the latter process including each of the given separators. All the produced combinations are then added to the growing set of words. If a year or a range of years has been specified, WoG can add different year transformations to the growing set of words. For example, given the year ABCD, possible transformations to the word foo could be fooCD, fooBCD, fooABCD, CDfoo, BCDfoo, ABCDfoo, ABfooCD. Almost the same goes for age transformations.

WoG, a simple Wordlist Generator written in Python

F M Lauria
2018

Abstract

WoG is a python tool designed to generate wordlists by applying some transformations to a small set of input words. How does it work? WoG starts by reading few words from an input file, then it applies to each word read an additivite trasformation, in order to further expand the starting word set. When the expansion of the initial set has been completed, WoG starts combininig each word with the others. If some separators have been specified, WoG repeats the latter process including each of the given separators. All the produced combinations are then added to the growing set of words. If a year or a range of years has been specified, WoG can add different year transformations to the growing set of words. For example, given the year ABCD, possible transformations to the word foo could be fooCD, fooBCD, fooABCD, CDfoo, BCDfoo, ABCDfoo, ABfooCD. Almost the same goes for age transformations.
2018
Istituto di informatica e telematica - IIT
wordlists
cybersecurity
python
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/439513
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