Delete run_data_processing.py

[Anton969969]

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": "[email protected]",

"password": [

"090698d",

"090698D"

]

},

{

"edit_distance": [

0,

1

],

"email": "[email protected]",

"password": [

"5555555555q",

"5555555555Q"

]

}

EXPLANATION: ReducePasswordsOnSimilarEmailsCallback/ contains files sorted by the first 2 letters of

the email address. For example [email protected] will be located in 96_per_user.json

Each file lists all the passwords grouped by user and by edit distance.

For example, [email protected] 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/
# --max_num_files 100 --output_folder ~/BreachCompilationAnalysis2
arg_p = parser.parse_args()
process(breach_compilation_folder=arg_p.breach_compilation_folder,
num_files=arg_p.max_num_files,
output_folder=arg_p.output_folder,
on_file_read_call_back_class=ReducePasswordsOnSimilarEmailsCallback)

if name == 'main':
run()