:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

Try in Splunk Security Cloud

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

  • Last Updated: 2021-01-26
  • Author: Bhavin Patel, Splunk
  • ID: 2a9b80d3-6220-4345-b5ad-290bf5d0d222

Annotations

ATT&CK
Kill Chain Phase
  • Exploitation
NIST
  • DE.DP
  • DE.AE
CIS20
  • CIS 13
CVE
1
2
3
4
5
6
7
8
9
10
`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`

Macros

The SPL above uses the following Macros:

:information_source: detect_spike_in_aws_security_hub_alerts_for_user_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

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

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.

Known False Positives

None

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 tbd

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

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