This search identifies endpoints that have caused a relatively high number of account lockouts in a short period.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Change
- Last Updated: 2020-11-09
- Author: David Dorsey, Splunk
- ID: c026e3dd-7e18-4abb-8f41-929e836efe74
Kill Chain Phase
- CIS 16
1 2 3 4 5 6 7 8 | 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 nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.dest All_Changes.result |`drop_dm_object_name("All_Changes")` |`drop_dm_object_name("Account_Management")` | `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:
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.
List of fields required to use this analytic.
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
https://splunkbase.splunk.com/app/3411/, 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.
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
|36.0||60||60||Multiple accounts have been locked out. Review $dest$ and results related to $user$.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 5