Try in Splunk Security Cloud

Description

This analytic employs the 3-sigma approach to identify distributed password spray attacks. A distributed password spray attack is a type of brute force attack where the attacker attempts a few common passwords against many different accounts, connecting from multiple IP addresses to avoid detection. 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: Hunting
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Authentication
  • Last Updated: 2023-11-01
  • Author: Dean Luxton
  • ID: b1a82fc8-8a9f-4344-9ec2-bde5c5331b57

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.AE
CIS20
  • CIS 10
CVE 


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21



| tstats `security_content_summariesonly` dc(Authentication.user) AS unique_accounts dc(Authentication.src) as unique_src count(Authentication.user) as total_failures from datamodel=Authentication.Authentication where Authentication.action="failure" by 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=sourcetype+"__"+signature_id 
| eventstats values(counter) as fnscounter 
| eval counter=coalesce(counter,fnscounter) ``` 3-sigma detection logic ``` 
| eventstats avg(unique_accounts) as comp_avg_user , stdev(unique_accounts) as comp_std_user avg(unique_src) as comp_avg_src , stdev(unique_src) as comp_std_src by counter 
| eval upperBoundUser=(comp_avg_user+comp_std_user*3), upperBoundsrc=(comp_avg_src+comp_std_src*3) 
| eval isOutlier=if((unique_accounts > 30 and unique_accounts >= upperBoundUser) and (unique_src > 30 and unique_accounts >= upperBoundsrc), 1, 0) 
| replace "::ffff:*" with * in src 
| where isOutlier=1 
| foreach * [ eval <<FIELD>> = if(<<FIELD>>="null",null(),<<FIELD>>)] 
| table _time, action, unique_src, unique_accounts, total_failures, sourcetype, signature_id 
| sort - total_failures 
| `detect_distributed_password_spray_attempts_filter`

Macros

The SPL above uses the following Macros:

:information_source: detect_distributed_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 that all relevant authentication data is mapped to the Common Information Model (CIM) and that the src field is populated with the source device information. Additionally, ensure that fill_nullvalue is set within the security_content_summariesonly macro to include authentication events from log sources that do not feature the signature_id field in the results.

Known False Positives

It is common to see a spike of legitimate failed authentication events on monday mornings.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
49.0 70 70 Distributed Password Spray Attempt Detected from $src$

: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