Detection: Detect Spike in AWS Security Hub Alerts for User

EXPERIMENTAL DETECTION

This detection status is set to experimental. The Splunk Threat Research team has not yet fully tested, simulated, or built comprehensive datasets for this detection. As such, this analytic is not officially supported. If you have any questions or concerns, please reach out to us at research@splunk.com.

Updated Date: 2024-10-09 ID: 2a9b80d3-6220-4345-b5ad-290bf5d0d222 Author: Bhavin Patel, Splunk Type: Anomaly Product: Splunk Enterprise Security

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.

Search

 1`aws_securityhub_finding` "findings{}.Resources{}.Type"= AwsIamUser 
 2| rename findings{}.Resources{}.Id as user 
 3| bucket span=4h _time 
 4| stats count AS alerts by _time user 
 5| eventstats avg(alerts) as total_launched_avg, stdev(alerts) as total_launched_stdev 
 6| eval threshold_value = 2 
 7| eval isOutlier=if(alerts > total_launched_avg+(total_launched_stdev * threshold_value), 1, 0) 
 8| search isOutlier=1 
 9| table _time user alerts 
10|`detect_spike_in_aws_security_hub_alerts_for_user_filter`

Data Source

Name	Platform	Sourcetype	Source
AWS Security Hub	AWS	`'aws:securityhub:finding'`	`'aws_securityhub_finding'`

Macros Used

Name	Value
aws_securityhub_finding	`sourcetype="aws:securityhub:finding"`
detect_spike_in_aws_security_hub_alerts_for_user_filter	`search *`

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

Annotations

- MITRE ATT&CK

+ Kill Chain Phases

+ NIST

+ CIS

- Threat Actors

ID	Technique	Tactic

NistCategory.DE_AE

Cis18Value.CIS_13

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting	Value
Disabled	true
Cron Schedule	`0 * * * *`
Earliest Time	`-70m@m`
Latest Time	`-10m@m`
Schedule Window	`auto`
Creates Risk Event	True

This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

Implementation

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

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
Spike in AWS Security Hub alerts for user - $user$	25	50	50

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

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	Not Applicable	N/A	N/A	N/A
Unit	❌ Failing	N/A	`N/A`	`N/A`
Integration	❌ Failing	N/A	`N/A`	`N/A`

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: GitHub | Version: 5