Detection: Detect Password Spray Attack Behavior On User

Description

The following analytic identifies any user failing to authenticate from 10 or more unique sources. This behavior could represent an adversary performing a Password Spraying attack to obtain initial access or elevate privileges. This logic can be used for real time security monitoring as well as threat hunting exercises. Environments can be very different depending on the organization. Test and customize this detections thresholds as needed

Search

 1
 2| tstats `security_content_summariesonly` max(_time) as lastTime, min(_time) as firstTime, values(Authentication.user_category) as user_category values(Authentication.src_category) as src_category values(Authentication.app) as app count from datamodel=Authentication.Authentication where * by Authentication.action,Authentication.src,Authentication.user 
 3| `drop_dm_object_name("Authentication")` 
 4| eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), success=if(action="success",count,0), src=upper(src), success_src=if(action="success",src,null), failure=if(action="failure",count,0), failed_src=if(action="failure",src,null) 
 5| `detect_password_spray_attack_behavior_on_user_filter` 
 6| stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as src_category values(success_src) as src values(failed_src) as failed_src dc(success_src) as success_dc dc(failed_src) as failed_dc dc(src) as src_dc, sum(failure) as failure, sum(success) as success by user 
 7| fields - _time 
 8| where src_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc 
 9| `security_content_ctime(firstTime)` 
10| `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

This detection requires ingesting authentication data to the appropriate accelerated datamodel. Recommend adjusting the search time window for this correlation to match the number of unique users (user_dc) in hours. i.e. 10 users over 10hrs

Known False Positives

Domain controllers, authentication chokepoints, and vulnerability scanners.

Associated Analytic Story

• Compromised User Account

Risk Based Analytics (RBA)

References

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

• https://www.microsoft.com/en-us/security/blog/2020/04/23/protecting-organization-password-spray-attacks/

• https://github.com/MarkoH17/Spray365

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