Detection: Azure AD Unusual Number of Failed Authentications From Ip

Description

The following analytic identifies a single source IP failing to authenticate with multiple valid users, potentially indicating a Password Spraying attack against an Azure Active Directory tenant. It uses Azure SignInLogs data and calculates the standard deviation for source IPs, applying the 3-sigma rule to detect unusual numbers of failed authentication attempts. This activity is significant as it may signal an adversary attempting to gain initial access or elevate privileges. If confirmed malicious, this could lead to unauthorized access, privilege escalation, and potential compromise of sensitive information.

1`azure_monitor_aad`  category=SignInLogs properties.status.errorCode=50126 properties.authenticationDetails{}.succeeded=false 
2| rename properties.* as * 
3| bucket span=5m _time 
4| stats  dc(userPrincipalName) AS unique_accounts values(userPrincipalName) as userPrincipalName by _time, ipAddress 
5| eventstats  avg(unique_accounts) as ip_avg, stdev(unique_accounts) as ip_std by ipAddress 
6| eval  upperBound=(ip_avg+ip_std*3) 
7| eval  isOutlier=if(unique_accounts > 10 and unique_accounts >= upperBound, 1,0) 
8| where isOutlier = 1 
9| `azure_ad_unusual_number_of_failed_authentications_from_ip_filter`

Data Source

Name Platform Sourcetype Source Supported App
Azure Active Directory Azure icon Azure 'azure:monitor:aad' 'Azure AD' N/A

Macros Used

Name Value
azure_monitor_aad sourcetype=azure:monitor:aad
azure_ad_unusual_number_of_failed_authentications_from_ip_filter search *
azure_ad_unusual_number_of_failed_authentications_from_ip_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
T1586 Compromise Accounts Resource Development
T1586.003 Cloud Accounts Resource Development
T1110 Brute Force Credential Access
T1110.003 Password Spraying Credential Access
T1110.004 Credential Stuffing Credential Access
KillChainPhase.EXPLOITAITON
KillChainPhase.WEAPONIZATION
NistCategory.DE_AE
Cis18Value.CIS_10
APT29
APT28
APT38
APT39
DarkVishnya
Dragonfly
FIN5
Fox Kitten
HEXANE
OilRig
Turla
APT28
APT29
APT33
Chimera
HEXANE
Lazarus Group
Leafminer
Silent Librarian
Chimera

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 True
This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

Implementation

You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Active Directory events into your Splunk environment through an EventHub. This analytic was written to be used with the azure:monitor:aad sourcetype leveraging the Signin log category.

Known False Positives

A source Ip failing to authenticate with multiple users is not a common for legitimate behavior.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Possible Password Spraying attack against Azure AD from source ip $ipAddress$ 54 60 90
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 AD azure:monitor:aad
Integration ✅ Passing Dataset Azure AD 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: 4