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  1. The proxy events from Squid logs need to be ingested in real-time.
  2. The proxy logs must be parsed into a standardized JSON structure that Metron can understand.
  3. In real-time, the Squid proxy event needs to be enriched so that the domain names are enriched with the IP information.
  4. In real-time, the IP within the proxy event must be checked for threat intel feeds.
  5. If there is a threat intel hit, an alert needs to be raised.
  6. The system should provide the ability to configure rules via a custom DSL to prioritize/score different types of alerts. 
  7. The end user must be able to see the new telemetry events completely enriched from the new data source. But most importantly, the user should be able to see the alerts prioritized by the high priority with the corresponding contextual data.  
  8. All of these requirements will need to be implemented easily without writing any new Java code.

But wait....Customer Foo still wants more.  Customer Foo wants to extract intelligence from the Squid telemetry stream and apply this intelligence in real-time to the threat triaging function.  The additional requirements are:

  1. Be able to profile my data and incorporate statistical features of the profile into my threat triage rules
  2. Be able to deploy a machine learning model that derives additional insights from the stream
  3. Incorporate my machine learning model into my threat triage rule along with threat intel, static rules, and statistical rules


What is Squid?

Squid is a caching proxy for the Web supporting HTTP, HTTPS, FTP, and more. It reduces bandwidth and improves response times by caching and reusing frequently-requested web pages. For more information on Squid see