: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 the creation of EC2 instances with previously unseen instance types. The detection is made by using a Splunk query to identify the EC2 instances. First, the query searches for changes in the EC2 instance creation action and filters for instances with instance types that are not recognized or previously seen. Next, the query uses the Splunk tstats command to gather the necessary information from the Change data model. Then, it filters the instances with unknown instance types and reviews previously seen instance types to determine if they are new or not. The detection is important because it identifies attackers attempting to create instances with unknown or potentially compromised instance types, which can be an attempt to gain unauthorized access to sensitive data, compromise of systems, exfiltrate data, potential disruption of services, or launch other malicious activities within the environment. False positives might occur since there might be legitimate reasons for creating instances with previously unseen instance types. Therefore, you must carefully review and triage all alerts.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Change
  • Last Updated: 2020-09-12
  • Author: David Dorsey, Splunk
  • ID: c6ddbf53-9715-49f3-bb4c-fb2e8a309cda

Annotations

ATT&CK
Kill Chain Phase
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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| tstats earliest(_time) as firstTime, latest(_time) as lastTime values(All_Changes.object_id) as dest, count from datamodel=Change where All_Changes.action=created by All_Changes.Instance_Changes.instance_type, All_Changes.user 
| `drop_dm_object_name("All_Changes")` 
| `drop_dm_object_name("Instance_Changes")` 
| where instance_type != "unknown" 
| lookup previously_seen_cloud_compute_instance_types instance_type as instance_type OUTPUTNEW firstTimeSeen, enough_data 
| eventstats max(enough_data) as enough_data 
| where enough_data=1 
| eval firstTimeSeenInstanceType=min(firstTimeSeen) 
| where isnull(firstTimeSeenInstanceType) OR firstTimeSeenInstanceType > relative_time(now(), "-24h@h") 
| table firstTime, user, dest, count, instance_type 
| `security_content_ctime(firstTime)` 
| `cloud_compute_instance_created_with_previously_unseen_instance_type_filter`

Macros

The SPL above uses the following Macros:

:information_source: cloud_compute_instance_created_with_previously_unseen_instance_type_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Lookups

The SPL above uses the following Lookups:

Required fields

List of fields required to use this analytic.

  • _time
  • All_Changes.object_id
  • All_Changes.action
  • All_Changes.Instance_Changes.instance_type
  • All_Changes.user

How To Implement

You must be ingesting your cloud infrastructure logs from your cloud provider. You should run the baseline search Previously Seen Cloud Compute Instance Types - Initial to build the initial table of instance types observed and times. You must also enable the second baseline search Previously Seen Cloud Compute Instance Types - Update to keep this table up to date and to age out old data. You can also provide additional filtering for this search by customizing the cloud_compute_instance_created_with_previously_unseen_instance_type_filter macro.

Known False Positives

It is possible that an admin will create a new system using a new instance type that has never been used before. Verify with the creator that they intended to create the system with the new instance type.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
30.0 50 60 User $user$ is creating an instance $dest$ with an instance type $instance_type$ that has not been previously seen.

: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: 1