Detection: Abnormally High Number Of Cloud Infrastructure API Calls

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: 2024-10-17 ID: 0840ddf1-8c89-46ff-b730-c8d6722478c0 Author: David Dorsey, Splunk Type: Anomaly Product: Splunk Enterprise Security

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.

 1
 2| 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 
 3| `drop_dm_object_name("All_Changes")` 
 4| eval HourOfDay=strftime(_time, "%H") 
 5| eval HourOfDay=floor(HourOfDay/4)*4 
 6| eval DayOfWeek=strftime(_time, "%w") 
 7| eval isWeekend=if(DayOfWeek >= 1 AND DayOfWeek <= 5, 0, 1) 
 8| join user HourOfDay isWeekend [ summary cloud_excessive_api_calls_v1] 
 9| where cardinality >=16 
10| apply cloud_excessive_api_calls_v1 threshold=0.005 
11| rename "IsOutlier(api_calls)" as isOutlier 
12| where isOutlier=1 
13| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) 
14| where api_calls > expected_upper_threshold 
15| eval distance_from_threshold = api_calls - expected_upper_threshold 
16| table _time, user, command, api_calls, expected_upper_threshold, distance_from_threshold 
17| `abnormally_high_number_of_cloud_infrastructure_api_calls_filter`

Data Source

Name Platform Sourcetype Source
AWS CloudTrail AWS icon AWS 'aws:cloudtrail' 'aws_cloudtrail'

Macros Used

Name Value

| abnormally_high_number_of_cloud_infrastructure_api_calls_filter | search * |

abnormally_high_number_of_cloud_infrastructure_api_calls_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
T1078.004 Cloud Accounts Defense Evasion
T1078 Valid Accounts Initial Access
KillChainPhase.DELIVERY
KillChainPhase.EXPLOITAITON
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_13
APT28
APT29
APT33
APT5
Ke3chang
LAPSUS$
APT18
APT28
APT29
APT33
APT39
APT41
Akira
Axiom
Carbanak
Chimera
Cinnamon Tempest
Dragonfly
FIN10
FIN4
FIN5
FIN6
FIN7
FIN8
Fox Kitten
GALLIUM
INC Ransom
Indrik Spider
Ke3chang
LAPSUS$
Lazarus Group
Leviathan
OilRig
POLONIUM
PittyTiger
Play
Sandworm Team
Silence
Silent Librarian
Star Blizzard
Suckfly
Threat Group-3390
Volt Typhoon
Wizard Spider
menuPass

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 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

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
user $user$ has made $api_calls$ api calls, violating the dynamic threshold of $expected_upper_threshold$ with the following command $command$. 15 30 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 ✅ Passing Dataset aws_cloudtrail aws:cloudtrail
Integration ✅ Passing Dataset aws_cloudtrail aws:cloudtrail

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: 4