: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.

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Description

The following analytic detects a spike in the number of API calls made to your cloud infrastructure by a user. It leverages cloud infrastructure logs and compares the current API call volume against a baseline probability density function to identify anomalies. This activity is significant because an unusual increase in API calls can indicate potential misuse or compromise of cloud resources. If confirmed malicious, this could lead to unauthorized access, data exfiltration, or disruption of cloud services, posing a significant risk to the organization's cloud environment.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Change
  • Last Updated: 2024-08-16
  • Author: David Dorsey, Splunk
  • ID: 0840ddf1-8c89-46ff-b730-c8d6722478c0

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1078.004 Cloud Accounts Defense Evasion, Persistence, Privilege Escalation, Initial Access
T1078 Valid Accounts Defense Evasion, Persistence, Privilege Escalation, Initial Access
Kill Chain Phase
  • Exploitation
  • Installation
  • Delivery
NIST
  • DE.AE
CIS20
  • CIS 13
CVE 


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| tstats count as api_calls values(All_Changes.command) as command from datamodel=Change where All_Changes.user!=unknown All_Changes.status=success by All_Changes.user _time span=1h 
| `drop_dm_object_name("All_Changes")` 
| eval HourOfDay=strftime(_time, "%H") 
| eval HourOfDay=floor(HourOfDay/4)*4 
| eval DayOfWeek=strftime(_time, "%w") 
| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
| join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] 
| where cardinality >=16 
| apply cloud_excessive_api_calls_v1 threshold=0.005 
| rename "IsOutlier(api_calls)" as isOutlier 
| where isOutlier=1 
| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) 
| where api_calls > expected_upper_threshold 
| eval distance_from_threshold = api_calls - expected_upper_threshold 
| table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold 
| `abnormally_high_number_of_cloud_infrastructure_api_calls_filter`

Macros

The SPL above uses the following Macros:

:information_source: abnormally_high_number_of_cloud_infrastructure_api_calls_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
  • All_Changes.command
  • All_Changes.user
  • All_Changes.status

How To Implement

You must be ingesting your cloud infrastructure logs. You also must run the baseline search Baseline Of Cloud Infrastructure API Calls Per User to create the probability density function.

Known False Positives

None.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
15.0 30 50 user $user$ has made $api_calls$ api calls, violating the dynamic threshold of $expected_upper_threshold$ with the following command $command$.

: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