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Deep Learning model to analyze a large corpus of clear text passwords.
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Today
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Momentum
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growth rate 0.05%/day
# 1.4 Billion Text Credentials Analysis (NLP)
Using deep learning and NLP to analyze a large corpus of clear text passwords.
Objectives:
- Train a generative model.
- Understand how people change their passwords over time: hello123 -> h@llo123 -> h@llo!23.
Disclaimer: for research purposes only.
## In the press
- [1.4 Billion Clear Text Credentials Discovered in a Single Database](https://medium.com/4iqdelvedeep/1-4-billion-clear-text-credentials-discovered-in-a-single-database-3131d0a1ae14)
- [Collection of 1.4 Billion Plain-Text Leaked Passwords Found Circulating Online](https://thehackernews.com/2017/12/data-breach-password-list.html)
- [Archive of 1.4 BEEELLION credentials in clear text found in dark web archive](https://www.theregister.co.uk/2017/12/12/archive_of_14_beeelion_credentials_in_clear_text_found_in_dark_web_archive/)
- [Forbes](https://www.forbes.com/sites/leemathews/2017/12/11/billion-hacked-passwords-dark-web/#74a6cf4221f2)
## Get the data
- Download any Torrent client.
- Here is a magnet link you can find on [Reddit](https://www.reddit.com/r/privacy/comments/7j1hhz/14_billion_clear_text_credentials_discovered_in_a/):
- magnet:?xt=urn:btih:7ffbcd8cee06aba2ce6561688cf68ce2addca0a3&dn=BreachCompilation&tr=udp%3A%2F%2Ftracker.openbittorrent.com%3A80&tr=udp%3A%2F%2Ftracker.leechers-paradise.org%3A6969&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Fglotorrents.pw%3A6969&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337
- Checksum list is available here: [checklist.chk](checklist.chk)
- `./count_total.sh` in `BreachCompilation` should display something like 1,400,553,870 rows.
## Get started (processing + deep learning)
Process the data and run the first deep learning model:
```
# make sure to install the python deps first. Virtual env are recommended here.
# virtualenv -p python3 venv3; source venv3/bin/activate; pip install -r requirements.txt
# Remove "--max_num_files 100" to process the whole dataset (few hours and 50GB of free disk space are required.)
./process_and_train.sh <BreachCompilation path>
```
## Data (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/reduce-passwords-on-similar-emails/99_per_user.json
- BreachCompilationAnalysis/reduce-passwords-on-similar-emails/9j_per_user.json
- BreachCompilationAnalysis/reduce-passwords-on-similar-emails/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",
Excerpt of 5,485 characters
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Philippe Rémy · Imperial College London
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dfe8878ece58fb43, topic:deep-learning, topic:tensorflow
matched fp:dfe8878ece58fb43, topic:natural-language-processing