ASL AWS Excessive Security Scanning
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
This search looks for AWS CloudTrail events and analyse the amount of eventNames which starts with Describe by a single user. This indicates that this user scans the configuration of your AWS cloud environment.
- Type: Anomaly
-
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2023-06-01
- Author: Patrick Bareiss, Splunk
- ID: ff2bfdbc-65b7-4434-8f08-d55761d1d446
Annotations
Kill Chain Phase
- Exploitation
NIST
- DE.AE
CIS20
- CIS 13
CVE
Search
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`amazon_security_lake` api.operation=Describe* OR api.operation=List* OR api.operation=Get*
| stats dc(api.operation) as dc_api_operations min(_time) as firstTime max(_time) as lastTime values(http_request.user_agent) as http_request.user_agent values(src_endpoint.ip) as src_endpoint.ip values(cloud.region) as cloud.region values(identity.user.account_uid) as identity.user.account_uid by identity.user.name
| where dc_api_operations > 50
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
|`asl_aws_excessive_security_scanning_filter`
Macros
The SPL above uses the following Macros:
asl_aws_excessive_security_scanning_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.
- api.operation
- identity.user.account_uid
- identity.user.name
- http_request.user_agent
- src_endpoint.ip
How To Implement
You must install Splunk Add-On for AWS Version v7.0.0 (https://splunkbase.splunk.com/app/1876) that includes includes a merge of all the capabilities of the Splunk Add-on for Amazon Security Lake. This search works with Amazon Security Lake logs which are parsed in the Open Cybersecurity Schema Framework (OCSF)format.
Known False Positives
While this search has no known false positives.
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
18.0 | 30 | 60 | user $identity.user.name$ has excessive number of api calls. |
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