Detection rules › Splunk
Windows Increase in Group or Object Modification Activity
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.
Known false positives
- No false positives have been identified at this time.
MITRE ATT&CK coverage
| Tactic | Techniques |
|---|---|
| Persistence | |
| Privilege Escalation | |
| Defense Impairment |
Telemetry coverage
Rule body
name: Windows Increase in Group or Object Modification Activity
id: 4f9564dd-a204-4f22-b375-4dfca3a68731
version: 10
creation_date: '2024-07-01'
modification_date: '2026-05-13'
author: Dean Luxton
status: production
type: TTP
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.
data_source:
- Windows Event Log Security 4663
search: |-
`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`
how_to_implement: Run this detection looking over a 7 day timeframe for best results.
known_false_positives: No false positives have been identified at this time.
references: []
finding:
title: Spike in Group or Object Modifications performed by $src_user$
entity:
field: src_user
type: user
score: 50
analytic_story:
- Sneaky Active Directory Persistence Tricks
asset_type: Endpoint
mitre_attack_id:
- T1098
- T1685
product:
- Splunk Enterprise
- Splunk Enterprise Security
- Splunk Cloud
category: endpoint
security_domain: audit
Stages and Predicates
Stage 1: search
`wineventlog_security` EventCode IN (4670,4727,4731,4734,4735,4764)
Stage 2: bucket
| bucket span=5m _time
Stage 3: stats
| 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
Stage 4: eventstats
| eventstats avg(objectCount) as comp_avg, stdev(objectCount) as comp_std
BY src_user, signature
Stage 5: eval
| eval upperBound=(comp_avg+comp_std)
Stage 6: eval
| eval isOutlier=if(objectCount > 10 and (objectCount >= upperBound), 1, 0)
isOutlier =if
objectCount > 10 AND objectCount >= upperBound1else
0Stage 7: search
| search isOutlier=1
Stage 8: search
| `windows_increase_in_group_or_object_modification_activity_filter`
Indicators
These rows show field, operator, and value matches.
| Field | Kind | Values | Search |
|---|---|---|---|
EventCode | in |
| field:"EventID" kind:in |
isOutlier | eq |
| field:"isOutlier" kind:eq value:"1" |