⚠️ WARNING THIS IS A EXPERIMENTAL DETECTION

We have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

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Description

This search looks for a spike in number of of AWS security Hub alerts for an AWS IAM User in 4 hours intervals.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel:
  • Last Updated: 2021-01-26
  • Author: Bhavin Patel, Splunk
  • ID: 2a9b80d3-6220-4345-b5ad-290bf5d0d222
`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser 
| rename findings{}.Resources{}.Id as user 
| bucket span=4h _time 
| stats count AS alerts by _time user 
| eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev 
| eval threshold_value = 2 
| eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) 
| search isOutlier=1 
| table _time user alerts 
|`detect_spike_in_aws_security_hub_alerts_for_user_filter`

Associated Analytic Story

How To Implement

You must install the AWS App for Splunk (version 5.1.0 or later) and Splunk Add-on for AWS (version 4.4.0 or later), then configure your Security Hub inputs. The threshold_value should be tuned to your environment and schedule these searches according to the bucket span interval.

Required field

  • _time
  • findings{}.Resources{}.Type
  • indings{}.Resources{}.Id
  • user

Kill Chain Phase

Known False Positives

None

Reference

Test Dataset

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

source | version: 3