:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

Try in Splunk Security Cloud

Description

This search will detect more than 5 login failures in Office365 Azure Active Directory from a single source IP address. Please adjust the threshold value of 5 as suited for your environment.

  • Type: Anomaly

  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2020-12-16
  • Author: Bhavin Patel, Splunk
  • ID: 7f398cfb-918d-41f4-8db8-2e2474e02222

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1110.001 Password Guessing Credential Access
T1110 Brute Force Credential Access
Kill Chain Phase
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


1
2
3
4



`o365_management_activity` Operation=UserLoginFailed  record_type=AzureActiveDirectoryStsLogon app=AzureActiveDirectory 
| stats count dc(user) as accounts_locked values(user) as user values(LogonError) as LogonError values(authentication_method) as authentication_method values(signature) as signature values(UserAgent) as UserAgent by src_ip record_type Operation app 
| search accounts_locked >= 5
| `high_number_of_login_failures_from_a_single_source_filter`

Macros

The SPL above uses the following Macros:

:information_source: high_number_of_login_failures_from_a_single_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
  • Operation
  • record_type
  • app
  • user
  • LogonError
  • authentication_method
  • signature
  • UserAgent
  • src_ip
  • record_type

How To Implement

Known False Positives

unknown

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 tbd

: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