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The following analytic detects endpoints causing a high number of account lockouts within a short period. It leverages the Windows security event logs ingested into the Change datamodel, specifically under the Account_Management node, to identify and count lockout events. This activity is significant as it may indicate a brute-force attack or misconfigured system causing repeated authentication failures. If confirmed malicious, this behavior could lead to account lockouts, disrupting user access and potentially indicating an ongoing attack attempting to compromise user credentials.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Change
  • Last Updated: 2024-05-19
  • Author: David Dorsey, Splunk
  • ID: c026e3dd-7e18-4abb-8f41-929e836efe74




ID Technique Tactic
T1078 Valid Accounts Defense Evasion, Persistence, Privilege Escalation, Initial Access
T1078.002 Domain Accounts Defense Evasion, Persistence, Privilege Escalation, Initial Access
Kill Chain Phase
  • Exploitation
  • Installation
  • Delivery
  • DE.AE
  • CIS 10
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_Changes.user) as user from datamodel=Change.All_Changes where All_Changes.result="*lock*" by All_Changes.dest All_Changes.result 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| search count > 5 
| `detect_excessive_account_lockouts_from_endpoint_filter`


The SPL above uses the following Macros:

:information_source: detect_excessive_account_lockouts_from_endpoint_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
  • All_Changes.user
  • nodename
  • All_Changes.result
  • All_Changes.dest

How To Implement

You must ingest your Windows security event logs in the Change datamodel under the nodename is Account_Management, for this search to execute successfully. Please consider updating the cron schedule and the count of lockouts you want to monitor, according to your environment. Splunk>Phantom Playbook Integration If Splunk>Phantom is also configured in your environment, a Playbook called "Excessive Account Lockouts Enrichment and Response" can be configured to run when any results are found by this detection search. The Playbook executes the Contextual and Investigative searches in this Story, conducts additional information gathering on Windows endpoints, and takes a response action to shut down the affected endpoint. To use this integration, install the Phantom App for Splunk, add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search, and set the corresponding Playbook to active. Playbook Link:

Known False Positives

It's possible that a widely used system, such as a kiosk, could cause a large number of account lockouts.

Associated Analytic Story


Risk Score Impact Confidence Message
36.0 60 60 Multiple accounts have been locked out. Review $dest$ and results related to $user$.

: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.


Test Dataset

Replay any dataset to Splunk Enterprise by using our tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

source | version: 9