Detection: Detect Distributed Password Spray Attempts

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.

 1
 2| tstats `security_content_summariesonly` dc(Authentication.user) AS unique_accounts dc(Authentication.src) as unique_src values(Authentication.app) as app values(Authentication.src) as src count(Authentication.user) as total_failures from datamodel=Authentication.Authentication where Authentication.action="failure" NOT Authentication.src IN ("-","unknown") Authentication.user_agent="*" by Authentication.signature_id, Authentication.user_agent, sourcetype, _time  span=10m 
 3| `drop_dm_object_name("Authentication")` ```fill out time buckets for 0-count events during entire search length``` 
 4| appendpipe [
 5| timechart limit=0 span=10m count 
 6| table _time] 
 7| fillnull value=0 unique_accounts, unique_src ``` Create aggregation field & apply to all null events``` 
 8| eval counter=sourcetype+"__"+signature_id 
 9| eventstats values(counter) as fnscounter 
10| eval counter=coalesce(counter,fnscounter)  
11| stats values(total_failures) as total_failures values(signature_id) as signature_id values(src) as src values(sourcetype) as sourcetype values(app) as app count by counter unique_accounts unique_src user_agent _time
12  ``` remove 0 count rows where counter has data```
13
14| sort - _time unique_accounts 
15| dedup _time counter ``` 3-sigma detection logic ``` 
16| 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 user_agent 
17| eval upperBoundUser=(comp_avg_user+comp_std_user*3), upperBoundsrc=(comp_avg_src+comp_std_src*3) 
18| eval isOutlier=if((unique_accounts > 30 and unique_accounts >= upperBoundUser) and (unique_src > 30 and unique_src >= upperBoundsrc), 1, 0) 
19| replace "::ffff:*" with * in src 
20| where isOutlier=1 
21| foreach * 
22    [ eval <<FIELD>> = if(<<FIELD>>="null",null(),<<FIELD>>)] 
23
24| mvexpand src  
25| iplocation src  
26| table _time, unique_src, unique_accounts, total_failures, sourcetype, signature_id, user_agent, src, Country 
27| eval date_wday=strftime(_time,"%a"), date_hour=strftime(_time,"%H") 
28| `detect_distributed_password_spray_attempts_filter`

Data Source

Name Platform Sourcetype Source
Azure Active Directory Sign-in activity Azure icon Azure 'azure:monitor:aad' 'Azure AD'

Macros Used

Name Value
security_content_summariesonly summariesonly=summariesonly_config allow_old_summaries=oldsummaries_config fillnull_value=fillnull_config``
detect_distributed_password_spray_attempts_filter search *
detect_distributed_password_spray_attempts_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1110.003 Password Spraying Credential Access
T1110 Brute Force Credential Access
KillChainPhase.EXPLOITAITON
NistCategory.DE_AE
Cis18Value.CIS_10
APT28
APT29
APT33
Agrius
Chimera
Ember Bear
HEXANE
Lazarus Group
Leafminer
Silent Librarian
APT28
APT38
APT39
APT41
Agrius
DarkVishnya
Dragonfly
Ember Bear
FIN5
Fox Kitten
HEXANE
OilRig
Turla

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting Value
Disabled true
Cron Schedule 0 * * * *
Earliest Time -70m@m
Latest Time -10m@m
Schedule Window auto
Creates Risk Event False
This configuration file applies to all detections of type hunting.

Implementation

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

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Distributed Password Spray Attempt Detected from $src$ 49 70 70
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation Passing N/A N/A N/A
Unit Passing Dataset azure:monitor:aad azure:monitor:aad
Integration ✅ Passing Dataset azure:monitor:aad azure:monitor:aad

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