Detection: Abnormally High Number Of Cloud Instances Launched

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: 2024-10-22 ID: f2361e9f-3928-496c-a556-120cd4223a65 Author: David Dorsey, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

The following analytic detects an abnormally high number of cloud instances launched within a 4-hour period. It leverages cloud infrastructure logs and applies a probability density model to identify outliers based on historical data. This activity is significant for a SOC because a sudden spike in instance creation could indicate unauthorized access or misuse of cloud resources. If confirmed malicious, this behavior could lead to resource exhaustion, increased costs, or provide attackers with additional compute resources to further their objectives.

 1
 2| tstats count as instances_launched values(All_Changes.object_id) as object_id from datamodel=Change where (All_Changes.action=created) AND All_Changes.status=success AND All_Changes.object_category=instance 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 HourOfDay isWeekend [summary cloud_excessive_instances_created_v1] 
 9| where cardinality >=16 
10| apply cloud_excessive_instances_created_v1 threshold=0.005 
11| rename "IsOutlier(instances_launched)" as isOutlier 
12| where isOutlier=1 
13| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0) 
14| eval distance_from_threshold = instances_launched - expected_upper_threshold 
15| table _time, user, instances_launched, expected_upper_threshold, distance_from_threshold, object_id 
16| `abnormally_high_number_of_cloud_instances_launched_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_instances_launched_filter | search * |

abnormally_high_number_of_cloud_instances_launched_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_10
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 Instances Launched to create the probability density function.

Known False Positives

Many service accounts configured within an AWS infrastructure are known to exhibit this behavior. Please adjust the threshold values and filter out service accounts from the output. Always verify if this search alerted on a human user.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
tbd 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