Abnormally High Number Of Cloud Instances Destroyed
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.
Description
The following analytic identifies an abnormally high number of cloud instances being destroyed within a 4-hour period. It leverages cloud infrastructure logs and applies a probability density model to detect outliers. This activity is significant for a SOC because a sudden spike in destroyed instances could indicate malicious activity, such as an insider threat or a compromised account attempting to disrupt services. If confirmed malicious, this could lead to significant operational disruptions, data loss, and potential financial impact due to the destruction of critical cloud resources.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Change
- Last Updated: 2024-05-27
- Author: David Dorsey, Splunk
- ID: ef629fc9-1583-4590-b62a-f2247fbf7bbf
Annotations
ATT&CK
Kill Chain Phase
- Exploitation
- Installation
- Delivery
NIST
- DE.AE
CIS20
- CIS 10
CVE
Search
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| tstats count as instances_destroyed values(All_Changes.object_id) as object_id from datamodel=Change where All_Changes.action=deleted AND All_Changes.status=success AND All_Changes.object_category=instance 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 HourOfDay isWeekend [summary cloud_excessive_instances_destroyed_v1]
| where cardinality >=16
| apply cloud_excessive_instances_destroyed_v1 threshold=0.005
| rename "IsOutlier(instances_destroyed)" as isOutlier
| where isOutlier=1
| eval expected_upper_threshold = mvindex(split(mvindex(BoundaryRanges, -1), ":"), 0)
| eval distance_from_threshold = instances_destroyed - expected_upper_threshold
| table _time, user, instances_destroyed, expected_upper_threshold, distance_from_threshold, object_id
| `abnormally_high_number_of_cloud_instances_destroyed_filter`
Macros
The SPL above uses the following Macros:
abnormally_high_number_of_cloud_instances_destroyed_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.object_id
- All_Changes.action
- All_Changes.status
- All_Changes.object_category
- All_Changes.user
How To Implement
You must be ingesting your cloud infrastructure logs. You also must run the baseline search Baseline Of Cloud Instances Destroyed
to create the probability density function.
Known False Positives
Many service accounts configured within a cloud 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
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
25.0 | 50 | 50 | tbd |
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: 2