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Description

This search detects user accounts that have been locked out a relatively high number of times in a short period.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Change
  • Last Updated: 2020-07-21
  • Author: David Dorsey, Splunk
  • ID: 95a7f9a5-6096-437e-a19e-86f42ac609bd

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1078 Valid Accounts Defense Evasion, Persistence, Privilege Escalation, Initial Access
T1078.003 Local Accounts Defense Evasion, Persistence, Privilege Escalation, Initial Access
Kill Chain Phase
  • Exploitation
NIST
  • PR.IP
CIS20
  • CIS 16
CVE
1
2
3
4
5
6
7
8
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where nodename=All_Changes.Account_Management All_Changes.result="lockout" by All_Changes.user 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_user_account_lockouts_filter`

Macros

The SPL above uses the following Macros:

:information_source: detect_excessive_user_account_lockouts_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.result
  • nodename
  • All_Changes.user

How To Implement

ou 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.

Known False Positives

It is possible that a legitimate user is experiencing an issue causing multiple account login failures leading to lockouts.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
36.0 60 60 Multiple accounts have been locked out. Review $nodename$ and $result$ 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.

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: 3