Detect Password Spray Attack Behavior From Source
Description
The following analytic identifies one source failing to authenticate with 10 or more unique users. 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 and works well against any number of data sources ingested into the CIM datamodel. Environments can be very different depending on the organization. Test and customize this detections thresholds if needed.
Query · spl
| 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
BY Authentication.action Authentication.app Authentication.authentication_method
Authentication.dest Authentication.signature Authentication.signature_id
Authentication.src Authentication.user
| `drop_dm_object_name("Authentication")`
| eval user=case((match(upper(user),"[a-zA-Z0-9]{3}")),upper(user),true(),null), src=upper(src), success=if(action="success",count,0),success_user=if(action="success",user,null),failure=if(action="failure",count,0), failed_user=if(action="failure",user,null)
| stats count min(firstTime) as firstTime max(lastTime) as lastTime values(app) as app values(src_category) as src_category values(success_user) as user values(failed_user) as failed_user dc(success_user) as success_dc dc(failed_user) as failed_dc dc(user) as user_dc ,sum(failure) as failure,sum(success) as success
BY src
| fields - _time
| where user_dc >= 10 AND .25 > (success/failure) AND failed_dc > success_dc
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `detect_password_spray_attack_behavior_from_source_filter`
Implementation guide
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.
Analyst notes
Known false positives: Domain controllers, authentication chokepoints, and vulnerability scanners.