Velociraptor to BigQuery

Overview

Our BigQuery output allows you to send Velociraptor hunt results to a BigQuery table allowing SQL-like queries against the hunt data. This is very similar to using Velociraptor notebooks, allowing you to perform hunt analysis at scale against massive datasets. For guidance on using LimaCharlie to execute Velociraptor hunts, see Velociraptor Extension.

Imagine you wanted to obtain running processes from 10s, 100s, or 1000s of systems using Velociraptor. You could easily issue a Windows.System.Pslist hunt across these systems, and let LimaCharlie push Velociraptor to the endpoints and collect the results. The issue is, if you want to run queries against all of the data returned by the hunts, you'll need a database-like tool to do that which is where BigQuery comes in.

BigQuery dataset containing Velociraptor hunt results:

Steps to Accomplish

1. You will need a Google Cloud project

2. You will need to create a service account within your Google Cloud project

   1. Navigate to your project

   2. Navigate to IAM

   3. Navigate to Service Accounts > Create Service Account

   4. Click on newly created Service Account and create a new key

      1.  

      2. This will provide you with the JSON format secret key you will later setup in your LimaCharlie output

   5. In BigQuery, create a Dataset, Table, & Schema similar to the screenshot below

      1.  

3. Now we're ready to create our LimaCharlie tailored output

   1. In the side navigation menu, click "Outputs" then add a new output

      1. Output stream: Tailored

      2. Destination: Google Cloud BigQuery

         1. Name: bigquery-tailored

            1. You can change this, but it affects a subsequent step so take note of the output name

         2. schema: sid:STRING, job_id:STRING, artifact:JSON

         3. Dataset: whatever you named BQ your dataset above

         4. Table: whatever you named your BQ table above

         5. Project: your GCP project name

         6. Secret Key: provide the JSON secret key for your GCP service account

         7. Advanced Options

            1. Custom Transform: paste in this JSON

               {
               "sid": "event.sid",
               "job_id": "event.job_id",
               "artifact": "{{ json .event.collection }}"
               }
               

            2. Specific Event Types: velociraptor_collection

      3.  

4. We now need a rule that will watch for Velociraptor collections send send them to the new tailored output

   1. Create a new D&R rule

      1. Detection

         event: velociraptor_collection
         op: exists
         path: event/collection
         

      2. Response

         - action: output
           name: bigquery-tailored # must match the output name you created earlier
         - action: report
           name: Velociraptor hunt sent to BigQuery
         

5. You are now ready to send Velociraptor hunts to BigQuery!

BigQuery Tips

Query Examples

Once the data arrives in BigQuery, it will be in three simple columns: sid, job_id, and artifact. The artifact column contains the raw JSON of the hunt results from each sensor that returned results.

Let's say we wanted to split out all results of a Windows.System.Pslist hunt so that each process, from each system, is returned in it's own row. Here is an example notebook to accomplish this:

SELECT 
  sid,
  json_extract_scalar(obj, '$.Name') as Name,
  json_extract_scalar(obj, '$.Exe') as Exe,
  json_extract_scalar(obj, '$.CommandLine') as CommandLine,
  json_extract_scalar(obj, '$.Authenticode.Trusted') as Authenticode,
  json_extract_scalar(obj, '$.Hash.SHA256') as SHA256,
  json_extract_scalar(obj, '$.Pid') as Pid,
  json_extract_scalar(obj, '$.Ppid') as Ppid,
  json_extract_scalar(obj, '$.Username') as Username
FROM 
  `lc-demo-infra.velociraptor.hunts`,
  UNNEST(json_extract_array(artifact.Windows_System_Pslist)) as obj
LIMIT 1000

Be sure to swap out lc-demo-infra.velociraptor.hunts for your own project.dataset.table names.

This results in the following view of our data

Suppose we wanted to perform some stacking analysis to identify the rarest combinations of Exe and CommandLine; the following query could help:

SELECT 
  json_extract_scalar(obj, '$.Exe') as Exe,
  json_extract_scalar(obj, '$.CommandLine') as CommandLine,
  COUNT(*) as Count
FROM 
  `lc-demo-infra.velociraptor.hunts`,
  UNNEST(json_extract_array(artifact.Windows_System_Pslist)) as obj
GROUP BY 
  Exe,
  CommandLine
ORDER BY 
  Count ASC

This results in the following view of our data

Now let's say you wanted to look for only processes that are Authenticode = untrusted, you would use a query such as this:

SELECT 
  sid,
  json_extract_scalar(obj, '$.Name') as Name,
  json_extract_scalar(obj, '$.Exe') as Exe,
  json_extract_scalar(obj, '$.CommandLine') as CommandLine,
  json_extract_scalar(obj, '$.Authenticode.Trusted') as Authenticode,
  json_extract_scalar(obj, '$.Hash.SHA256') as SHA256,
  json_extract_scalar(obj, '$.Pid') as Pid,
  json_extract_scalar(obj, '$.Ppid') as Ppid,
  json_extract_scalar(obj, '$.Username') as Username
FROM 
  `lc-demo-infra.velociraptor.hunts`,
  UNNEST(json_extract_array(artifact.Windows_System_Pslist)) as obj
WHERE
  json_extract_scalar(obj, '$.Authenticode.Trusted') = 'untrusted'
LIMIT 1000

WHERE Filters for Specific Conditions

Here are some brief examples of WHERE statements to perform specific filtering.

String presence

This example checks for the presence of a string mimikatz appearing anywhere within CommandLine

WHERE
  STRPOS(json_extract_scalar(obj, '$.CommandLine'), 'mimikatz') > 0 AND

Compare integers

This example checks for the presence of an integer 0 in a numeric field GroupID

WHERE
  CAST(json_extract_scalar(obj, '$.GroupID') AS INT64) = 0

Parsing Nested JSON Objects

In the Windows.System.Pslist examples above, there are a few columns which contain nested JSON such as Authenticode and Hash. To expand these objects in their entirety in the corresponding column/row, we'd write a query like this:

SELECT 
  json_extract(obj, '$.Authenticode') as Authenticode, # json_extract to unpack nested json
  json_extract_scalar(obj, '$.Authenticode.Trusted') as Trusted,
  json_extract(obj, '$.Hash') as Hashes, # json_extract to unpack nested json
  json_extract_scalar(obj, '$.Hash.SHA256') as SHA256, # extract a specific field from the nested json
FROM 
  `lc-demo-infra.velociraptor.hunts`,
  UNNEST(json_extract_array(artifact.Windows_System_Pslist)) as obj
LIMIT 1000

See the output of this query below: