Pivotable Reverse WhoIs / PDNS Fusion with Registrant Tracking & Alerting plus API for automated queries (JSON/CSV/TXT)
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README.md

WhoDat Project

The WhoDat project is a front-end for whoisxmlapi data, or any whois data living in ElasticSearch. It integrates whois data, current IP resolutions and passive DNS. In addition to providing an interactive, pivotable application for analysts to perform research, it also has an API which will allow output in JSON or list format.

WhoDat was originally written by Chris Clark. The original implementation is in PHP and available in this repository under the legacy_whodat directory. The code was re-written from scratch by Wesley Shields and Murad Khan in Python, and is available under the pydat directory.

The PHP version is left for those who want to run it, but it is not as full featured or extensible as the Python implementation, and is not supported.

For more information on the PHP implementation please see the readme. For more information on the Python implementation keep reading...

PreReqs

pyDat is a python 2.7 application that requires the following to run:

  • ElasticSearch installed somewhere (versions 5.2 -> 6.x are supported, up to 6.3.1 tested)
  • python packages:
    • requests
    • unicodecsv
    • markdown
    • django
    • elasticsearch (version must correspond to cluster version)
    • ply

pyDat

pyDat is a Python implementation of Chris Clark's WhoDat code. It is designed to be more extensible and has more features than the PHP implementation.

To aid in properly populating the database, a script called elasticsearch_populate is provided to auto-populate the data. Note that the data coming from whoisxmlapi doesn't seem to be always consistent so some care should be taken when ingesting data. More testing needs to be done to ensure all data is ingested properly. Anyone setting up their database, should read the available flags for the script before running it to ensure they've tweaked it for their setup. The following is the output from elasticsearch_populate -h

usage: elasticsearch_populate.py [-h] [-f FILE | -d DIRECTORY] [-e EXTENSION]
                                (-i IDENTIFIER | -r | -z | --config-template-only)
                                [-v] [--vverbose] [-s]
                                [-x EXCLUDE | -n INCLUDE] [-o COMMENT]
                                [-u [ES_URI [ES_URI ...]]]
                                [--es-user ES_USER] [--es-pass ES_PASS]
                                [--es-ask-pass] [--es-enable-ssl ES_CACERT]
                                [--es-disable-sniffing] [-p INDEX_PREFIX]
                                [-B BULK_SIZE] [-b BULK_FETCH_SIZE]
                                [--rollover-size ROLLOVER_DOCS]
                                [--pipelines PIPELINES]
                                [--shipper-threads SHIPPER_THREADS]
                                [--fetcher-threads FETCHER_THREADS]
                                [--ignore-field-prefixes [IGNORE_FIELD_PREFIXES [IGNORE_FIELD_PREFIXES ...]]]
                                [--debug]

optional arguments:
-h, --help            show this help message and exit
-f FILE, --file FILE  Input CSV file
-d DIRECTORY, --directory DIRECTORY
                        Directory to recursively search for CSV files --
                        mutually exclusive to '-f' option
-e EXTENSION, --extension EXTENSION
                        When scanning for CSV files only parse files with
                        given extension (default: 'csv')
-i IDENTIFIER, --identifier IDENTIFIER
                        Numerical identifier to use in update to signify
                        version (e.g., '8' or '20140120')
-r, --redo            Attempt to re-import a failed import or import more
                        data, uses stored metadata from previous import (-o,
                        -n, and -x not required and will be ignored!!)
-z, --update          Run the script in update mode. Intended for taking
                        daily whois data and adding new domains to the current
                        existing index in ES.
--config-template-only
                        Configure the ElasticSearch template and then exit
-v, --verbose         Be verbose
--vverbose            Be very verbose (Prints status of every domain parsed,
                        very noisy)
-s, --stats           Print out Stats after running
-x EXCLUDE, --exclude EXCLUDE
                        Comma separated list of keys to exclude if updating
                        entry
-n INCLUDE, --include INCLUDE
                        Comma separated list of keys to include if updating
                        entry (mutually exclusive to -x)
-o COMMENT, --comment COMMENT
                        Comment to store with metadata
-u [ES_URI [ES_URI ...]], --es-uri [ES_URI [ES_URI ...]]
                        Location(s) of ElasticSearch Server (e.g.,
                        foo.server.com:9200) Can take multiple endpoints
--es-user ES_USER     Username for ElasticSearch when Basic Authis enabled
--es-pass ES_PASS     Password for ElasticSearch when Basic Authis enabled
--es-ask-pass         Prompt for ElasticSearch password
--es-enable-ssl ES_CACERT
                        The path, on disk to the cacert of the ElasticSearch
                        server to enable ssl/https support
--es-disable-sniffing
                        Disable ES sniffing, useful when ssl
                        hostnameverification is not working properly
-p INDEX_PREFIX, --index-prefix INDEX_PREFIX
                        Index prefix to use in ElasticSearch (default: pydat)
-B BULK_SIZE, --bulk-size BULK_SIZE
                        Size of Bulk Elasticsearch Requests
-b BULK_FETCH_SIZE, --bulk-fetch-size BULK_FETCH_SIZE
                        Number of documents to search for at a time (default
                        50), note that this will be multiplied by the number
                        of indices you have, e.g., if you have 10
                        pydat-<number> indices it results in a request for 500
                        documents
--rollover-size ROLLOVER_DOCS
                        Set the number of documents after which point a new
                        index should be created, defaults to 50 milllion, note
                        that this is fuzzy since the index count isn't
                        continuously updated, so should be reasonably below 2
                        billion per ES shard and should take your ES
                        configuration into consideration
--pipelines PIPELINES
                        Number of pipelines, defaults to 2
--shipper-threads SHIPPER_THREADS
                        How many threads per pipeline to spawn to send bulk ES
                        messages. The larger your cluster, the more you can
                        increase this, defaults to 1
--fetcher-threads FETCHER_THREADS
                        How many threads to spawn to search ES. The larger
                        your cluster, the more you can increase this, defaults
                        to 2
--ignore-field-prefixes [IGNORE_FIELD_PREFIXES [IGNORE_FIELD_PREFIXES ...]]
                        list of fields (in whois data) to ignore when
                        extracting and inserting into ElasticSearch
--debug               Enables debug logging

Note that when adding a new version of data to the database, you should use either the -x flag to exclude certain fields that are not important to track changes or the -n flag to include specific fields that are subject to scrutiny. This will significantly decrease the amount of data that is stored between versions. You can only use either -x or -n not both at the same time, but you can choose whichever is best for your given environment. As an example, if you get daily updates, you might decide that for daily updates you only care if contactEmail changes but every quarter you might want to instead only exclude certain fields you don't find important.

ScreenShot

alt tag

Running pyDat

pyDat does not provide any data on its own. You must provide your own whois data in an ElasticSearch data store.

Populating ElasticSearch with whoisxmlapi data (Ubuntu 16.04.3 LTS)

  • Install ElasticSearch. Using Docker is the easiest mechanism
  • Download latest trimmed (smallest possible) whoisxmlapi quarterly DB dump.
  • Extract the csv files.
  • Use the included script in the scripts/ directory:
./elasticsearch_populate.py -u localhost:9200 -f ~/whois/data/1.csv -i '1' -v -s -x Audit_auditUpdatedDate,updatedDate,standardRegUpdatedDate,expiresDate,standardRegExpiresDate

Upgrading ElasticSearch 5.x -> 6.x

If you started with Elasticsearch 5.x and upgrade your cluster to 6.x, you should run the population script with the --config-template-only flag to update the backend template. This will not change the way the data is configured and serves only to eliminate deprecation warnings that your cluster will throw otherwise

./elasticsearch_populate -u localhost:9200 --config-template-only

Installation

Local Installation

  • Copy pydat to /var/www/ (or prefered location)
  • Copy pydat/custom_settings_example.py to pydat/custom_settings.py.
  • Edit pydat/custom_settings.py to suit your needs.
    • Include your Passive DNS keys if you have any!
  • Configure Apache to use the provided wsgi interface to pydat.
sudo apt-get install libapache2-mod-wsgi
sudo vi /etc/apache2/sites-available/whois

<VirtualHost *:80>
        ServerName whois
        ServerAlias whois
        # Install Location
        WSGIScriptAlias / /var/www/pydat/wsgi.py
        Alias /static/ /var/www/pydat/pydat/static/
        <Location "/static/">
            Options -Indexes
        </Location>
</VirtualHost>

Docker Installation

If you don't want to install pyDat manually, you can use the docker image to quickly deploy the system.

First, make sure to copy custom_settings_example.py to custom_settings.py and customize it to match your environment

You can then launch pyDat by running

docker run -d --name pydat -p 80:80 -v <path/to/custom_settings.py>:/opt/WhoDat/pydat/pydat/custom_settings.py mitrecnd/pydat

Docker Compose

To run pydat with compose your docker-compose.yml file could look like:

version: '2'
services:
    pydat:
        image: mitrecnd/pydat
        volumes:
            - "./custom_settings.py:/opt/WhoDat/pydat/pydat/custom_settings.py"
        ports:
            - 80:80

Note that the above config assumes that a custom_settings.py file exists in the same directory as the compose file.

Docker Compose Test Setup

If you want to test pydat with a local docker-ized instance of ES, here is an example compose configuration:

version: '2'
services:
    elasticsearch:
        image: docker.elastic.co/elasticsearch/elasticsearch-oss:6.3.1
        environment:
          - cluster.name=pydat
          - bootstrap.memory_lock=true
          - "ES_JAVA_OPTS=-Xms512m -Xmx512m"
        ulimits:
          memlock:
            soft: -1
            hard: -1
        mem_limit: 1g
        volumes:
            - pydat-data:/usr/share/elasticsearch/data
        ports:
            - 127.0.0.1:9200:9200
    pydat:
        image: mitrecnd/pydat
        volumes:
            - "./custom_settings.py:/opt/WhoDat/pydat/pydat/custom_settings.py"
        ports:
            - 0.0.0.0:8888:80

volumes:
  pydat-data:

Along with the contents of its cooresponding custom_settings.py file:

DEBUG = True
ALLOWED_HOSTS = ['*']
ES_URI = 'elasticsearch:9200'

Note that the ElasticSearch instance is only accessible via localhost, while pydat will be listening on all interfaces on port 8888. Also, further note that while this is fine for small data sets, a production-level cluster is recommended for hosting full quarterly dumps.

pyDat API

The following endpoints are exposed:

ajax/metadata/
ajax/metadata/<version>/

The metadata endpoint returns metadata available for the data in the database. Specifying a version will return metadata for that specific version

ajax/domain/<domainName>/
ajax/domain/<domainName>/latest/
ajax/domain/<domainName>/<version>/
ajax/domain/<domainName>/<version1>/<version2>/
ajax/domain/<domainName>/diff/<version1>/<version2>/

The domain endpoint allows you to get information about a specific domain name. By default, this will return information for any version of a domain that is found in the database. You can specify more information to obtain specific versions of domain information or to obtain the latest entry. You can also obtain a diff between two versions of a domain to see what has changed.

ajax/domains/<searchKey>/<searchValue>/
ajax/domains/<searchKey>/<searchValue>/latest/
ajax/domains/<searchKey>/<searchValue>/<version>/
ajax/domains/<searchKey>/<searchValue>/<version1>/<version2>/

The domains endpoint allows you to search for domains based on a specified key. Currently the following keys are supported:

domainName
registrant_name
contactEmail
registrant_telephone

Similar to the domain endpoint you can specify what versions of the data you are looking for.

Example Queries:

curl http://pydat.myorg.domain/ajax/domain/google.com/latest/

curl http://pydat.myorg.domain/ajax/domains/domainName/google.com/

Advanced Syntax Endpoint

ajax/query

This endpoint takes 4 parameters via a GET request:

query - The query to search ES with
size - The number of elements to return (aka page size)
page - The page to return, combining this with size you can get the results in chunks
unique - Attempts to return the latest entry per domainName

Note on the unique parameter: If you're using the unique parameter, note that paging of results is disabled, but the size parameter will still be used to control the number of results returned.

Untested Stuff

Chris has an update.py script which I haven't used yet, so all bets are off, but it should allow you to get regular updates on specific watched fields via a cron job. For more information please see the PHP implementation.

TODO

  • Move Chris' update script to a common directory and test it out.

Legal Stuff

pyDat is copyright The MITRE Corporation 2018.

The PHP implementation is copyright Chris Clark, 2013. Contact him at Chris@xenosys.org.

The PHP and Python versions are licensed under the same license.

pyDat is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

pyDat is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with pyDat. If not, see http://www.gnu.org/licenses/.

Approved for Public Release; Distribution Unlimited 14-1633