ID | Technique | Tactic |
---|---|---|
T1078.004 | Cloud Accounts | Defense Evasion |
T1078 | Valid Accounts | Initial Access |
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.
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.
Search
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 | Supported App |
---|---|---|---|---|
AWS CloudTrail | AWS | 'aws:cloudtrail' |
'aws_cloudtrail' |
N/A |
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
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 |
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 |
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: 3