BigSnarf blog

Infosec FTW

Using Spark to do real-time large scale log analysis

Spark on IPython Notebook used for analytic workflows of the auth.log.

What is really cool about this Spark platform is that I can either batch or data mine the whole dataset on the cluster. Built on this idea. In the screenshot below, you can see me using my IPython Notebook for interactive query. All the code I create to investigate the auth.log can easily be converted to Spark Streaming DStream objects in Scala. Effectively, I can build a real-time application all from the same platform. “Cool” IMHO. In other posts you I will document pySpark batch, and DStream algorithms in Scala processing of auth.log Screen Shot 2014-03-26 at 8.56.29 PM These are some of the items I am filtering in my PySpark interactive queries in the Notebook

Successful user login “Accepted password”, “Accepted publickey”, “session opened”
Failed user login “authentication failure”, “failed password”
User log-off “session closed”
User account change or deletion “password changed”, “new user”, “delete user”
Sudo actions “sudo: … COMMAND=…” “FAILED su”
Service failure “failed” or “failure”

Note that ip address is making a ton of requests invalid   Maybe I should correlate to web server request logs too?

Excessive access attempts to non-existent files
Code (SQL, HTML) seen as part of the URL
Access to extensions you have not implemented
Web service stopped/started/failed messages
Access to “risky” pages that accept user input
Look at logs on all servers in the load balancer pool
Error code 200 on files that are not yours
Failed user authentication Error code 401, 403
Invalid request Error code 400
Internal server error Error code 500

Here is the data correlated to Successful Logins, Failed Logins and Failed logins to an invalid user. Notice the “” ip address. suspicious


Screen Shot 2014-03-28 at 12.52.46 PM Links


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