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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
Created
2026-03-10
Data Sources
AWS Security Hub
Tags
AWS Security Hub AlertsCritical Alerts
Raw Content
name: Detect Spike in AWS Security Hub Alerts for User
id: 2a9b80d3-6220-4345-b5ad-290bf5d0d222
version: 10
date: '2026-03-10'
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: []
rba:
message: Spike in AWS Security Hub alerts for user - $user$
risk_objects:
- field: user
type: user
score: 20
threat_objects: []
tags:
analytic_story:
- AWS Security Hub Alerts
- Critical Alerts
asset_type: AWS Instance
product:
- Splunk Enterprise
- Splunk Enterprise Security
- Splunk Cloud
security_domain: network