IVRE with Kibana

IVRE has an experimental backend for Elasticsearch for the view purpose (see Purposes). Only Elasticsearch 7 supported and tested for now.

While this backend lacks a lot of features, it is enough to create a view into an Elasticsearch cluster. Other tools using Elasticsearch can then use IVRE’s data.


As stated in the installation page (see the Python section), you will need to install the elasticsearch and elasticsearch-dsl Python packages.

View creation

About views

Views are created from Nmap, Masscan or Zgrab2 scan results (stored in the nmap purpose) and passive host intelligence collected by Zeek (stored in the passive purpose). That is a prerequisite of view creation so if you have not read it yet, you should go read Active recon and Passive first.

You can check you have data in the nmap and/or passive purposes using the command line: ivre scancli --count and ivre ipinfo --count.


We need to configure IVRE to use the Elasticsearch database for the view purpose. Since we want to do that only to create the view, we are going to create a dedicated IVRE configuration file, for example in ~/.ivre-elastic.conf; for example, to use an Elasticsearch server running on the local machine:

echo 'DB_VIEW = "elastic://"' > ~/.ivre-elastic.conf

Then, to use this dedicated configuration file, we just have to set the IVRE_CONF environment variable:

IVRE_CONF=~/.ivre-elastic.conf ivre view --count

Index creation & Data insertion

So now, we can create a view as we would do with any other backend. For example, if we want to create a view using all the records from the nmap and passive purposes:

IVRE_CONF=~/.ivre-elastic.conf ivre view --init < /dev/null
IVRE_CONF=~/.ivre-elastic.conf ivre db2view

The first command will drop any existing data, and create the index and mapping, and the second will create the view itself.

Using Kibana

From Kibana, you will have to create an index pattern (this can only be done after the view creation). The default index name from view is ivre-views; you can use this value as index pattern (and remove the final * since we use only one index).


The field starttime can be used as the “Time Filter field name”.


You are all set! Now, explore this data set as you would explore any other one.

For a couple of examples of how Kibana can be used to explore IVRE’s data see the Kibana exploration part of the screenshot gallery for examples of useful visualizations.

If you have any troubles with Kibana, please refer to its documentation.