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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.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Authentication
  • Last Updated: 2023-10-30
  • Author: Steven Dick
  • ID: b6391b15-e913-4c2c-8949-9eecc06efacc

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1110.003 Password Spraying Credential Access
T1110 Brute Force Credential Access
Kill Chain Phase
  • Exploitation
NIST
  • DE.CM
CIS20
  • CIS 10
CVE 


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| 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 
| `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) 
| `detect_password_spray_attack_behavior_from_source_filter` 
| 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)`

Macros

The SPL above uses the following Macros:

:information_source: detect_password_spray_attack_behavior_from_source_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.

  • _time
  • Authentication.user_category
  • Authentication.src_category
  • Authentication.app
  • Authentication.action
  • Authentication.src
  • Authentication.user

How To Implement

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

RBA

Risk Score Impact Confidence Message
60.0 80 75 The source [$src$] attempted to access $user_dc$ distinct users a total of $count$ times between [$firstTime$] and [$lastTime$]. $success$ successful logins detected.

:information_source: 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