Windows Increase in Group or Object Modification Activity
Description
This analytic detects an increase in modifications to AD groups or objects. Frequent changes to AD groups or objects can indicate potential security risks, such as unauthorized access attempts, impairing defences or establishing persistence. By monitoring AD logs for unusual modification patterns, this detection helps identify suspicious behavior that could compromise the integrity and security of the AD environment.
- Type: TTP
-
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2023-10-13
- Author: Dean Luxton
- ID: 4f9564dd-a204-4f22-b375-4dfca3a68731
Annotations
ATT&CK
Kill Chain Phase
- Installation
- Exploitation
NIST
- DE.CM
CIS20
- CIS 10
CVE
Search
1
2
3
4
5
6
7
8
`wineventlog_security` EventCode IN (4670,4727,4731,4734,4735,4764)
| bucket span=5m _time
| stats values(object) as object, dc(object) as objectCount, values(src_user_category) as src_user_category, values(dest) as dest, values(dest_category) as dest_category by _time, src_user, signature, status
| eventstats avg(objectCount) as comp_avg, stdev(objectCount) as comp_std by src_user, signature
| eval upperBound=(comp_avg+comp_std)
| eval isOutlier=if(objectCount > 10 and (objectCount >= upperBound), 1, 0)
| search isOutlier=1
| `windows_increase_in_group_or_object_modification_activity_filter`
Macros
The SPL above uses the following Macros:
windows_increase_in_group_or_object_modification_activity_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.
- EventCode
- src_user
- signature
How To Implement
Run this detection looking over a 7 day timeframe for best results.
Known False Positives
Unknown
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
8.0 | 20 | 40 | Spike in Group or Object Modifications performed by $src_user$ |
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