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Description

This analytic employs the 3-sigma approach to detect an unusual volume of failed authentication attempts from a single source. A password spray attack is a type of brute force attack where an attacker tries a few common passwords across many different accounts to avoid detection and account lockouts. By utilizing the Authentication Data Model, this detection is effective for all CIM-mapped authentication events, providing comprehensive coverage and enhancing security against these attacks.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Authentication
  • Last Updated: 2023-11-01
  • Author: Dean Luxton
  • ID: 086ab581-8877-42b3-9aee-4a7ecb0923af

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` dc(Authentication.user) AS unique_accounts values(Authentication.app) as app count(Authentication.user) as total_failures from datamodel=Authentication.Authentication where Authentication.action="failure" by Authentication.src, Authentication.action, Authentication.signature_id, sourcetype, _time  span=2m 
| `drop_dm_object_name("Authentication")` ```fill out time buckets for 0-count events during entire search length``` 
| appendpipe [
| timechart limit=0 span=5m count 
| table _time] 
| fillnull value=0 unique_accounts, unique_src ``` remove duplicate & empty time buckets``` 
| sort - total_failures 
| dedup _time ``` Create aggregation field & apply to all null events``` 
| eval counter=src+"__"+sourcetype+"__"+signature_id 
| eventstats values(counter) as fnscounter 
| eval counter=coalesce(counter,fnscounter) 
| eventstats avg(unique_accounts) as comp_avg , stdev(unique_accounts) as comp_std by counter 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_accounts > 30 and unique_accounts >= upperBound, 1, 0) 
| replace "::ffff:*" with * in src 
| where isOutlier=1 
| foreach * [ eval <<FIELD>> = if(<<FIELD>>="null",null(),<<FIELD>>)] 
| table _time, src, action, app, unique_accounts, total_failures, sourcetype, signature_id 
| `detect_password_spray_attempts_filter`

Macros

The SPL above uses the following Macros:

:information_source: detect_password_spray_attempts_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.

  • Authentication.action
  • Authentication.user
  • Authentication.src

How To Implement

Ensure in-scope authentication data is CIM mapped and the src field is populated with the source device. Also ensure fill_nullvalue is set within the macro security_content_summariesonly.

Known False Positives

Unknown

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
49.0 70 70 Potential Password Spraying attack from $src$ targeting $unique_accounts$ unique accounts.

: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