PyDomains: Classifying the Content of Domains¶
We use multiple large labeled datasets to categorize the kind of the content hosted by a domain using (for now) only information in the domain name. If the domain name is in the data, the package gives the corresponding category (or categories). If it isn’t, the package uses a trained model to predict the kind of content hosted by the domain.
Quick Start¶
import pandas as pd
from pydomains import *
# Get help
help(dmoz_cat)
# Load data
df = pd.read_csv('./pydomains/examples/input-header.csv')
# df
# label url
# 0 test1 topshop.com
# 1 test2 beyondrelief.com
# Get the Content Category from DMOZ, phishtank
df_dmoz = dmoz_cat(df, domain_names = 'url')
df_phish = phish_cat(df, domain_names = 'url')
# Predicted category from shallalist, toulouse
df_shalla = pred_shalla(df, domain_names = 'url')
df_toulouse = pred_toulouse(df, domain_names = 'url')
API¶
Using pydomains¶
>>> import pandas as pd
>>> from pydomains import *
Using TensorFlow backend.
>>> # Get help of the function
... help(dmoz_cat)
Help on function dmoz_cat in module pydomains.dmoz_cat:
dmoz_cat(df, domain_names='domain_names', year=2016, latest=False)
Appends DMOZ domain categories to the DataFrame.
The function extracts the domain name along with the subdomain
from the specified column and appends the category (dmoz_cat)
to the DataFrame. If DMOZ file is not available locally or
latest is set to True, it downloads the file. The function
looks for category of the domain name in the DMOZ file
for each domain. When no match is found, it returns an
empty string.
Args:
df (:obj:`DataFrame`): Pandas DataFrame. No default value.
domain_names (str): Column name of the domain in DataFrame.
Default in `domain_names`.
year (int): DMOZ data year. Only 2016 data is available.
Default is 2016.
latest (Boolean): Whether or not to download latest
data available from GitHub. Default is False.
Returns:
DataFrame: Pandas DataFrame with two additional columns:
'dmoz_year_domain' and 'dmoz_year_cat'
>>> # Load an example input with columns header
... df = pd.read_csv('./pydomains/examples/input-header.csv')
>>> df
label url
0 test1 topshop.com
1 test2 beyondrelief.com
2 test3 golf-tours.com/test
3 test4 thegayhotel.com
4 test5 https://zonasequravlabcp.com/bcp/
5 test6 http://privatix.xyz
6 test7 adultfriendfinder.com
7 test8 giftregistrylocator.com
8 test9 bangbrosonline.com
9 test10 scotland-info.co.uk
>>> # Get the Content Category from DMOZ
... df = dmoz_cat(df, domain_names='url')
Loading DMOZ data file...
>>> df
label url dmoz_2016_domain \
0 test1 topshop.com topshop.com
1 test2 beyondrelief.com beyondrelief.com
2 test3 golf-tours.com/test golf-tours.com
3 test4 thegayhotel.com thegayhotel.com
4 test5 https://zonasequravlabcp.com/bcp/ zonasequravlabcp.com
5 test6 http://privatix.xyz privatix.xyz
6 test7 adultfriendfinder.com adultfriendfinder.com
7 test8 giftregistrylocator.com giftregistrylocator.com
8 test9 bangbrosonline.com bangbrosonline.com
9 test10 scotland-info.co.uk scotland-info.co.uk
dmoz_2016_cat
0 Top/Regional/Europe/United_Kingdom/Business_an...
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
8 NaN
9 Top/Regional/Europe/United_Kingdom/Scotland/Tr...
>>> # Predict Content Category Using the Toulouse Model
... df = pred_toulouse(df, domain_names='url')
Loading Toulouse model, vocab and names data file...
>>> df
label url dmoz_2016_domain \
0 test1 topshop.com topshop.com
1 test2 beyondrelief.com beyondrelief.com
2 test3 golf-tours.com/test golf-tours.com
3 test4 thegayhotel.com thegayhotel.com
4 test5 https://zonasequravlabcp.com/bcp/ zonasequravlabcp.com
5 test6 http://privatix.xyz privatix.xyz
6 test7 adultfriendfinder.com adultfriendfinder.com
7 test8 giftregistrylocator.com giftregistrylocator.com
8 test9 bangbrosonline.com bangbrosonline.com
9 test10 scotland-info.co.uk scotland-info.co.uk
dmoz_2016_cat \
0 Top/Regional/Europe/United_Kingdom/Business_an...
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
8 NaN
9 Top/Regional/Europe/United_Kingdom/Scotland/Tr...
pred_toulouse_2017_domain pred_toulouse_2017_lab \
0 topshop.com shopping
1 beyondrelief.com adult
2 golf-tours.com shopping
3 thegayhotel.com adult
4 zonasequravlabcp.com phishing
5 privatix.xyz adult
6 adultfriendfinder.com adult
7 giftregistrylocator.com shopping
8 bangbrosonline.com adult
9 scotland-info.co.uk shopping
pred_toulouse_2017_prob_adult pred_toulouse_2017_prob_audio-video \
0 0.133953 0.003793
1 0.521590 0.016359
2 0.186083 0.008208
3 0.971451 0.001080
4 0.065503 0.001063
5 0.986328 0.002241
6 0.939441 0.000211
7 0.014645 0.000570
8 0.945490 0.004017
9 0.256270 0.003745
pred_toulouse_2017_prob_bank pred_toulouse_2017_prob_gambling \
0 1.161209e-04 2.911613e-04
1 3.912278e-03 6.484169e-03
2 1.783388e-03 8.022175e-04
3 8.920387e-05 6.256429e-05
4 6.226773e-04 1.073759e-04
5 6.823016e-07 1.969112e-06
6 1.742063e-07 6.485808e-08
7 3.973934e-04 1.019526e-05
8 9.122109e-05 1.142884e-04
9 3.962536e-04 4.977396e-04
pred_toulouse_2017_prob_games pred_toulouse_2017_prob_malware \
0 0.002073 0.003976
1 0.022408 0.018371
2 0.013352 0.006392
3 0.000713 0.000934
4 0.012431 0.077391
5 0.001021 0.004949
6 0.000044 0.000059
7 0.004112 0.016339
8 0.002216 0.000422
9 0.014452 0.006615
pred_toulouse_2017_prob_others pred_toulouse_2017_prob_phishing \
0 0.014862 0.112132
1 0.046011 0.172208
2 0.021287 0.060633
3 0.005018 0.017201
4 0.031691 0.416989
5 0.003069 0.002094
6 0.001674 0.058497
7 0.015631 0.131174
8 0.017964 0.012574
9 0.057622 0.111698
pred_toulouse_2017_prob_press pred_toulouse_2017_prob_publicite \
0 8.404775e-04 0.000761
1 2.525988e-02 0.002821
2 1.853482e-02 0.000990
3 2.208834e-04 0.000135
4 2.796387e-03 0.000284
5 4.559151e-06 0.000252
6 1.133891e-07 0.000007
7 1.115335e-02 0.000436
8 5.098383e-04 0.000785
9 7.331154e-04 0.000168
pred_toulouse_2017_prob_shopping
0 0.727203
1 0.164577
2 0.681934
3 0.003094
4 0.391121
5 0.000038
6 0.000066
7 0.805531
8 0.015817
9 0.547802
Models¶
For more information about the models, including the decisions we made around curtailing the number of categories, see here
Underlying Data¶
We use data from DMOZ, Shallalist, PhishTank, and a prominent Blacklist aggregator. For more details about how the underlying data, see here
Validation¶
We compare content categories according to the TrustedSource API with content category from Shallalist and the Shallalist model for all the unique domains in the comScore 2004 data:
Notes and Caveats¶
- The DMOZ categorization system at tier 1 is bad. The category names are vague. They have a lot of subcategories that could easily belong to other tier 1 categories. That means a) it would likely be hard to classify well at tier 1 and b) not very valuable. So we choose not to predict tier 1 DMOZ categories.
- The association between patterns in domain names and the kind of
content they host may change over time. It may change as new domains
come online and as older domains are repurposed. All this likely
happens slowly. But, to be careful, we add a
yearvariable in our functions. Each list and each model is for a particular year. - Imputing the kind of content hosted by a domain may suggest to some that domains carry only one kind of content. Many domains don’t. And even when they do, the quality varies immensely. So we can predict at the URL level also. And it is something we hope to add over time. See TODO for our plans.
- There are a lot of categories where we do not expect domain names to not have any systematic patterns. Rather than make noisy predictions using just the data from domain names, we plan to tackle this prediction task with some additional data. See TODO for our plans.
Authors¶
Suriyan Laohaprapanon and Gaurav Sood
Contributor Code of Conduct¶
The project welcomes contributions from everyone! In fact, it depends on it. To maintain this welcoming atmosphere, and to collaborate in a fun and productive way, we expect contributors to the project to abide by the Contributor Code of Conduct
License¶
The package is released under the MIT License.