A Python package and CLI for parsing aggregate and forensic DMARC reports

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  • License: Apache Software License
  • Author: Sean Whalen
  • Tags DMARC, parser, reporting
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Project description

parsedmarc

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A screenshot of DMARC summary charts in Kibana{.align-center}

parsedmarc is a Python module and CLI utility for parsing DMARC reports. When used with Elasticsearch and Kibana (or Splunk), it works as a self-hosted open source alternative to commercial DMARC report processing services such as Agari Brand Protection, Dmarcian, OnDMARC, ProofPoint Email Fraud Defense, and Valimail.

Help Wanted

This is a project is maintained by one developer. Please consider reviewing the open issues to see how you can contribute code, documentation, or user support. Assistance on the pinned issues would be particularly helpful.

Thanks to all contributors!

Features

  • Parses draft and 1.0 standard aggregate/rua reports
  • Parses forensic/failure/ruf reports
  • Can parse reports from an inbox over IMAP, Microsoft Graph, or Gmail API
  • Transparently handles gzip or zip compressed reports
  • Consistent data structures
  • Simple JSON and/or CSV output
  • Optionally email the results
  • Optionally send the results to Elasticsearch and/or Splunk, for use with premade dashboards
  • Optionally send reports to Apache Kafka

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for Sean.Whalen from gravatar.com Sean.Whalen

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: Apache Software License
  • Author: Sean Whalen
  • Tags DMARC, parser, reporting
Classifiers

Release history Release notifications | RSS feed

8.16.0

8.15.4

8.15.3

8.15.2

8.15.1

8.15.0

8.14.1

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This version

8.3.1

8.3.0

8.2.0

8.1.1

8.1.0

8.0.3

8.0.2 yanked

Reason this release was yanked:

IMAP bugs

8.0.1 yanked

Reason this release was yanked:

IMAP bugs

8.0.0 yanked

Reason this release was yanked:

Broken packaging

7.1.1

7.1.0

7.0.1

7.0.0

6.12.0

6.11.0

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1.1.0

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1.0.4

1.0.3

1.0.2

1.0.1

1.0.0

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