from processing_callbacks import ReducePasswordsOnSimilarEmailsCallback
from utils import process
parser = argparse.ArgumentParser('Data Processing Tool.')
parser.add_argument('--breach_compilation_folder', type=str,
help='BreachCompilation/ folder containing the 1.4 billion passwords dataset.', required=True)
parser.add_argument('--output_folder', type=str,
default='~/BreachCompilationAnalysis',
help='Output folder containing the generated datasets.')
parser.add_argument('--max_num_files', type=int,
help='Maximum number of files to read. The entire dataset contains around 2000 files.'
'Can be useful to create mini datasets for the models.')
import argparse
from processing_callbacks import ReducePasswordsOnSimilarEmailsCallback from utils import process
parser = argparse.ArgumentParser('Data Processing Tool.') parser.add_argument('--breach_compilation_folder', type=str, help='BreachCompilation/ folder containing the 1.4 billion passwords dataset.', required=True) parser.add_argument('--output_folder', type=str, default='~/BreachCompilationAnalysis', help='Output folder containing the generated datasets.') parser.add_argument('--max_num_files', type=int, help='Maximum number of files to read. The entire dataset contains around 2000 files.' 'Can be useful to create mini datasets for the models.')
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EXPLANATION
------------------------------------------------------------------------------
INPUT: BreachCompilation/
BreachCompilation is organized as:
#
a/ - folder of emails starting with a
a/a - file of emails starting with aa
a/b
a/d
...
z/
...
z/y
z/z
------------------------------------------------------------------------------
OUTPUT: - BreachCompilationAnalysis/edit-distance/1.csv
- BreachCompilationAnalysis/edit-distance/2.csv
- BreachCompilationAnalysis/edit-distance/3.csv
[...]
> cat 1.csv
1 ||| samsung94 ||| samsung94@
1 ||| 040384alexej ||| 040384alexey
1 ||| HoiHalloDoeii14 ||| hoiHalloDoeii14
1 ||| hoiHalloDoeii14 ||| hoiHalloDoeii13
1 ||| hoiHalloDoeii13 ||| HoiHalloDoeii13
1 ||| 8znachnuu ||| 7znachnuu
EXPLANATION: edit-distance/ contains the passwords pairs sorted by edit distances.
1.csv contains all pairs with edit distance = 1 (exactly one addition, substitution or deletion).
2.csv => edit distance = 2, and so on.
#
- BreachCompilationAnalysis/ReducePasswordsOnSimilarEmailsCallback/99_per_user.json
- BreachCompilationAnalysis/ReducePasswordsOnSimilarEmailsCallback/9j_per_user.json
- BreachCompilationAnalysis/ReducePasswordsOnSimilarEmailsCallback/9a_per_user.json
[...]
> cat 96_per_user.json
{
"1.0": [
{
"edit_distance": [
0,
1
],
"email": "96-000@mail.ru",
"password": [
"090698d",
"090698D"
]
},
{
"edit_distance": [
0,
1
],
"email": "96-96.1996@mail.ru",
"password": [
"5555555555q",
"5555555555Q"
]
}
EXPLANATION: ReducePasswordsOnSimilarEmailsCallback/ contains files sorted by the first 2 letters of
the email address. For example 96-000@mail.ru will be located in 96_per_user.json
Each file lists all the passwords grouped by user and by edit distance.
For example, 96-000@mail.ru had 2 passwords: 090698d and 090698D. The edit distance between them is 1.
The edit_distance and the password arrays are of the same length, hence, a first 0 in the edit distance array.
Those files are useful to model how users change passwords over time.
We can't recover which one was the first password, but a shortest hamiltonian path algorithm is run
to detect the most probably password ordering for a user. For example:
hello => hello1 => hell@1 => hell@11 is the shortest path.
We assume that users are lazy by nature and that they prefer to change their password by the lowest number
of characters.
def run():
example: --breach_compilation_folder /media/philippe/DATA/BreachCompilation/
if name == 'main': run()