splunk_escuAnomaly

Detect Spike in AWS Security Hub Alerts for User

The following analytic identifies a spike in the number of AWS Security Hub alerts for an AWS IAM User within a 4-hour interval. It leverages AWS Security Hub findings data, calculating the average and standard deviation of alerts to detect significant deviations. This activity is significant as a sudden increase in alerts for a specific user may indicate suspicious behavior or a potential security incident. If confirmed malicious, this could signify an ongoing attack, unauthorized access, or misuse of IAM credentials, potentially leading to data breaches or further exploitation.

Detection Query

`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`

Author

Bhavin Patel, Splunk

Data Sources

AWS Security Hub

Raw Content

name: Detect Spike in AWS Security Hub Alerts for User
id: 2a9b80d3-6220-4345-b5ad-290bf5d0d222
version: 11
creation_date: '2020-08-06'
modification_date: '2026-05-13'
author: Bhavin Patel, Splunk
status: experimental
type: Anomaly
description: The following analytic identifies a spike in the number of AWS Security Hub alerts for an AWS IAM User within a 4-hour interval. It leverages AWS Security Hub findings data, calculating the average and standard deviation of alerts to detect significant deviations. This activity is significant as a sudden increase in alerts for a specific user may indicate suspicious behavior or a potential security incident. If confirmed malicious, this could signify an ongoing attack, unauthorized access, or misuse of IAM credentials, potentially leading to data breaches or further exploitation.
data_source:
    - AWS Security Hub
search: |-
    `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`
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: No false positives have been identified at this time.
references: []
intermediate_findings:
    entities:
        - field: user
          type: user
          score: 20
          message: Spike in AWS Security Hub alerts for user - $user$
analytic_story:
    - AWS Security Hub Alerts
    - Critical Alerts
asset_type: AWS Instance
mitre_attack_id: []
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: cloud
security_domain: network