Detection: Windows Multiple NTLM Null Domain Authentications

Description

The following analytic detects when a device is the target of numerous NTLM authentications using a null domain. This activity generally results when an attacker attempts to brute force, password spray, or otherwise authenticate to a domain joined Windows device from a non-domain device. This activity may also generate a large number of EventID 4776 events in tandem, however these events will not indicate the attacker or target device

Search

 1`ntlm_audit` EventCode IN (8004,8005,8006) DomainName=NULL UserName!=NULL 
 2| eval src = replace(WorkstationName,"\\\\","")  ```CIM alignment, remove leading \\ from some auth attempts ``` 
 3| eval dest = SChannelName, user = UserName ``` CIM alignment``` 
 4| where SChannelName!=src ``` Remove NTLM auths to self, improves accuracy for certain applications``` 
 5| `windows_multiple_ntlm_null_domain_authentications_filter` 
 6| stats count min(_time) as firstTime max(_time) as lastTime dc(eval(upper(user))) as unique_count dc(eval(upper(src))) as src_count by dest 
 7| eventstats avg(unique_count) as unique_avg , stdev(unique_count) as unique_std 
 8| eval upperBound_unique=(1+unique_avg+unique_std*3) ``` adjust formula for sensitivity``` 
 9| eval isOutlier=CASE(unique_count > upperBound_unique, 1, true(), 0) 
10| where isOutlier==1 
11| `security_content_ctime(firstTime)` 
12| `security_content_ctime(lastTime)`

Data Source

No data sources specified for this detection.

Macros Used

Annotations

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Implementation

The following analytic requires that NTLM Operational logs to be imported from the environment Domain Controllers. This requires configuration of specific auditing settings, see Microsoft references for further guidance. This analytic is specific to EventID 8004~8006.

Known False Positives

Applications that deal with non-domain joined authentications. Recommend adjusting the upperBound_unique eval for tailoring the correlation to your environment, running with a 24hr search window will smooth out some statistical noise.

Associated Analytic Story

• Active Directory Password Spraying

Risk Based Analytics (RBA)

References

• https://attack.mitre.org/techniques/T1110/003/

• https://techcommunity.microsoft.com/t5/ask-the-directory-services-team/ntlm-blocking-and-you-application-analysis-and-auditing/ba-p/397191

• https://techcommunity.microsoft.com/t5/microsoft-defender-for-identity/enriched-ntlm-authentication-data-using-windows-event-8004/m-p/871827

• https://www.varonis.com/blog/investigate-ntlm-brute-force

• https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-nrpc/4d1235e3-2c96-4e9f-a147-3cb338a0d09f

Detection Testing

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: GitHub | Version: 2