The project leverages docker-compose with a custom python script so you need to have the following packages installed in your machine:

In some systems you could find pre-installed older versions. Please check this and install a supported version before attempting the installation. Otherwise it would fail.


  • The project uses public docker image that is available on Docker Hub
  • IntelOwl is tested and supported to work in a Linux-based OS. It may also run on windows, but that is not officialy supported yet.


Obviously we strongly suggest to read through all the page to configure IntelOwl in the most appropriate way.

However, if you feel lazy, you could just install and test IntelOwl with the following steps. Be sure to run docker and python commands with sudo if permissions/roles have not been set

# clone the IntelOwl project repository
git clone
cd IntelOwl/

# construct environment files from templates
cd docker/
cp env_file_app_template env_file_app
cp env_file_postgres_template env_file_postgres
cp env_file_integrations_template env_file_integrations

# start the app
cd ..
python3 prod up

# create a super user 
docker exec -ti intelowl_uwsgi python3 createsuperuser

# now the app is running on http://localhost:80


There is a YouTube video that may help in the installation process. (ManySteps have changed since v2.0.0)

Deployment Components

IntelOwl is composed of various different services, namely:

  • Angular: Frontend (IntelOwl-ng)

  • Django: Backend

  • PostgreSQL: Database

  • Rabbit-MQ: Message Broker

  • Celery: Task Queue

  • Nginx: Reverse proxy for the Django API and web asssets.

  • Uwsgi: Application Server

  • Elastic Search (optional): Auto-sync indexing of analysis’ results.

  • Kibana (optional): GUI for Elastic Search. We provide a saved configuration with dashboards and visualizations.

  • Flower (optional): Celery Management Web Interface

All these components are managed via docker-compose.

Deployment Preparation

Open a terminal and execute below commands to construct new environment files from provided templates.

cd docker/
cp env_file_app_template env_file_app
cp env_file_postgres_template env_file_postgres
cp env_file_integrations_template env_file_integrations

Environment configuration (required)

In the env_file_app, configure different variables as explained below.

REQUIRED variables to run the image:

  • DB_HOST, DB_PORT, DB_USER, DB_PASSWORD: PostgreSQL configuration (The DB credentals should match the ones in the env_file_postgres).

Strongly recommended variable to set:

  • DJANGO_SECRET: random 50 chars key, must be unique. If you do not provide one, Intel Owl will automatically set a new secret on every run.

Optional variables needed to enable specific analyzers:


  • AUTH0_KEY: Auth0 API Key

  • SECURITYTRAILS_KEY: Securitytrails API Key

  • SHODAN_KEY: Shodan API key


  • GSF_KEY: Google Safe Browsing API key

  • OTX_KEY: Alienvault OTX API key

  • CIRCL_CREDENTIALS: CIRCL PDNS credentials in the format: user|pass

  • VT_KEY: VirusTotal API key

  • HA_KEY: HybridAnalysis API key

  • INTEZER_KEY: Intezer API key

  • INQUEST_API_KEY: InQuest API key



  • MISP_KEY: your own MISP instance key

  • MISP_URL: your own MISP instance URL


  • CUCKOO_URL: your cuckoo instance URL


  • CENSYS_API_ID & CENSYS_API_SECRET: Censys credentials


  • GREYNOISE_API_KEY: GreyNoise API (docs)

  • INTELX_API_KEY: IntelligenceX API (docs)

  • UNPAC_ME_API_KEY: UnpacMe API (docs)

  • IPINFO_KEY: ipinfo API key

  • ZOOMEYE_KEY: ZoomEye API Key(docs)

  • TRIAGE_KEY: API key(docs)

  • WIGLE_KEY: WiGLE API Key(docs)

  • XFORCE_KEY & XFORCE_PASSWORD: IBM X-Force Exchange API (docs)

  • MWDB_KEY: API key for MWDB

  • SSAPINET_KEY: (docs)


Advanced additional configuration:

  • OLD_JOBS_RETENTION_DAYS: Database retention for analysis results (default: 3 days). Change this if you want to keep your old analysis longer in the database.

Database configuration (required)

In the env_file_postgres, configure different variables as explained below.

Required variables:



  • POSTGRES_DB (default: intel_owl_db)

If you prefer to use an external PostgreSQL instance, you should just remove the relative image from the docker/default.yml file and provide the configuration to connect to your controlled instances.

Web server configuration (optional)

Intel Owl provides basic configuration for:

  • Nginx (configuration/intel_owl_nginx_http)

  • Uwsgi (configuration/intel_owl.ini)

In case you enable HTTPS, remember to set the environment variable HTTPS_ENABLED as “enabled” to increment the security of the application.

There are 3 options to execute the web server:

  • HTTP only (default)

    The project would use the default deployment configuration and HTTP only.

  • HTTPS with your own certificate

    The project provides a template file to configure Nginx to serve HTTPS: configuration/intel_owl_nginx_https.

    You should change ssl_certificate, ssl_certificate_key and server_name in that file.

    Then you should modify the nginx service configuration in docker/default.yml:

    • change intel_owl_nginx_http with intel_owl_nginx_https

    • in volumes add the option for mounting the directory that hosts your certificate and your certificate key.

  • HTTPS with Let’s Encrypt

    We provide a specific docker-compose file that leverages Traefik to allow fast deployments of public-faced and HTTPS-enabled applications.

    Before using it, you should configure the configuration file docker/traefik.override.yml by changing the email address and the hostname where the application is served. For a detailed explanation follow the official documentation: Traefix doc.

    After the configuration is done, you can add the option --traefik while executing the

Analyzers configuration (optional)

In the file configuration/analyzers_config.json there is the configuration for all the available analyzers you can run. For a complete list of all current available analyzer please look at: Usage

You may want to change this configuration to add new analyzers or to change the configuration of some of them.

The name of the analyzers can be changed at every moment based on your wishes. You just need to remember that it’s important that you keep at least the following keys in the analyzers dictionaries to let them run correctly:

  • type: can be file or observable. It specifies what the analyzer should analyze

  • python_module: path to the analyzer class


You can see the full list of all available analyzers in the Usage.html or Live Demo.


Some analyzers are kept optional and can easily be enabled. Refer to this part of the docs.

AWS support

At the moment there’s a basic support for some of the AWS services. More is coming in the future.


If you would like to run this project on AWS, I’d suggest you to use the “Secrets Manager” to store your credentials. In this way your secrets would be better protected.

This project supports this kind of configuration. Instead of adding the variables to the environment file, you should just add them with the same name on the AWS Secrets Manager and Intel Owl will fetch them transparently.

Obviously, you should have created and managed the permissions in AWS in advance and accordingly to your infrastructure requirements.

Also, you need to set the environment variable AWS_SECRETS to True to enable this mode.

You can customize the AWS Region changing the environment variable AWS_REGION.


If you like, you could use AWS SQS instead of Rabbit-MQ to manage your queues. In that case, you should change the parameter CELERY_BROKER_URL to sqs:// and give your instances on AWS the proper permissions to access it.

Also, you need to set the environment variable AWS_SQS to True to activate the additional required settings.

… More coming


Important Info

IntelOwl depends heavily on docker and docker compose so as to hide this complexity from the enduser the project leverages a custom script ( to interface with docker-compose.

You may invoke $ python3 –help to get help and usage info.

The CLI provides the primitives to correctly build, run or stop the containers for IntelOwl. Therefore,

  • It is possible to attach every optional docker container that IntelOwl has: multi_queue with traefik enabled while every optional docker analyzer is active.
  • It is possible to insert an optional docker argument that the CLI will pass to docker-compose

Now that you have completed different configurations, starting the containers is as simple as invoking:

$ python3 prod up

You can add the parameter -d to run the application in the background.


To stop the application you have to:

  • if executed without -d parameter: press CTRL+C

  • if executed with -d parameter: python3 prod down

Cleanup of database and application

This is a destructive operation but can be useful to start again the project from scratch

python3 prod down -v

After Deployment

Users creation

You may want to run docker exec -ti intelowl_uwsgi python3 createsuperuser after first run to create a superuser. Then you can add other users directly from the Django Admin Interface after having logged with the superuser account.

Django Groups & Permissions settings

Refer to this section of the docs.


Deploy on Remnux

Remnux is a Linux Toolkit for Malware Analysis.

IntelOwl and Remnux have the same goal: save the time of people who need to perform malware analysis or info gathering.

Therefore we suggest Remnux users to install IntelOwl to leverage all the tools provided by both projects in a unique environment.

To do that, you can follow the same steps detailed above for the installation of IntelOwl.

Update to the most recent version

To update the project with the most recent available code you have to follow these steps:

$ cd <your_intel_owl_directory> # go into the project directory
$ git pull # pull new changes
$ python3 prod stop # kill the currently running IntelOwl containers 
$ python3 prod up --build # restart the IntelOwl application

Rebuilding the project/ Creating custom docker build

If you make some code changes and you like to rebuild the project, follow these steps:

  1. python3 test build --tag=<your_tag> . to build the new docker image.

  2. Add this new image tag in the docker/test.override.yml file.

  3. Start the containers with python3 test up --build.

Updating to >=2.0.0 from a 1.x.x version

Users upgrading from previous versions need to manually move env_file_app, env_file_postgres and env_file_integrations files under the new docker directory.

Updating to >v1.3.x from any prior version

If you are updating to >v1.3.0 from any prior version, you need to execute a helper script so that the old data present in the database doesn’t break.

  1. Follow the above updation steps, once the docker containers are up and running execute the following in a new terminal

    docker exec -ti intelowl_uwsgi bash

    to get a shell session inside the IntelOwl’s container.

  2. Now just copy and paste the below command into this new session,

    curl | python -
  3. If you see “Update successful!”, everything went fine and now you can enjoy the new features!