Intel Owl v3.1 releases: analyze files, domains, IPs in multiple ways

Intel Owl

Do you want to get threat intelligence data about a file, an IP, or a domain?

Do you want to get this kind of data from multiple sources at the same time using a single API request?

You are in the right place!

This application is built to scale out and to speed up the retrieval of threat info.

It can be integrated easily in your stack of security tools to automate common jobs usually performed, for instance, by SOC analysts manually.

Intel Owl is composed of analyzers that can be run to retrieve data from external sources (like VirusTotal or AbuseIPDB) or to generate intel from internal analyzers (like Yara or Oletools)

This solution is for everyone who needs a single point to query for info about a specific file or observable (domain, IP, URL, hash).

Features

• Provides enrichment of threat intel for malware as well as observables (IP, Domain, URL, and hash).
• This application is built to scale out and to speed up the retrieval of threat info.
• It can be integrated easily in your stack of security tools (pyintelowl) to automate common jobs usually performed, for instance, by SOC analysts manually.
• Intel Owl is composed of analyzers that can be run to retrieve data from external sources (like VirusTotal or AbuseIPDB) or to generate intel from internal analyzers (like Yara or Oletools)
• API is written in Django and Python 3.7.
• Inbuilt frontend client: IntelOwl-ng provides features such as dashboard, visualizations of analysis data, easy to use forms for requesting new analysis, etc. Live Demo.

Available services or analyzers

You can see the full list of all available analyzers in the documentation or live demo.

Changelog v3.1

Notes:

• Update PyIntelOwl to version 4.1.0.
• Introducing IntelOwl Official Parternship & Sponsorship Tiers.
• IntelOwl now has an official integration in Tines templates.

REST API changes:

• /api/analyze_file and /api/analyze_observable:
  • Request Body:
    • deprecate md5 attribute. Will now be calculated on server.
    • deprecate tags_id attribute in favor of tags_labels. Previously, the tags_id attribute would accept a list of tag indices, now the tags_labels accepts a list of tag labels (non-existing Tag objects are created automatically with a randomly generated color).
    • observable_classification attribute is now optional. If not passed, the application tries to guess the correct classification using regular expressions.
  • Response Body: now also returns a connectors_running attribute that is a list of connectors executed for the specific job.

Misc:

• Added default parameters to entrypoint_flower.sh to allow retrocompatibility.
• Fixes in documentation.
• Bump some dependencies.

Install && Use

Copyright (C) 2019 Matteo Lodi