Detection: Detect Excessive User Account Lockouts

Updated Date: 2024-05-20 ID: 95a7f9a5-6096-437e-a19e-86f42ac609bd Author: David Dorsey, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

The following analytic identifies user accounts experiencing an excessive number of lockouts within a short timeframe. It leverages the 'Change' data model, specifically focusing on events where the result indicates a lockout. This activity is significant as it may indicate a brute-force attack or misconfiguration, both of which require immediate attention. If confirmed malicious, this behavior could lead to account compromise, unauthorized access, and potential lateral movement within the network.

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Change.All_Changes where  All_Changes.result="*lock*" by All_Changes.user All_Changes.result 
3|`drop_dm_object_name("All_Changes")` 
4|`drop_dm_object_name("Account_Management")`
5| `security_content_ctime(firstTime)` 
6| `security_content_ctime(lastTime)` 
7| search count > 5 
8| `detect_excessive_user_account_lockouts_filter`

Data Source

Name Platform Sourcetype Source Supported App
N/A N/A N/A N/A N/A

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
detect_excessive_user_account_lockouts_filter search *
detect_excessive_user_account_lockouts_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
T1078 Valid Accounts Defense Evasion
T1078.003 Local Accounts Initial Access
KillChainPhase.DELIVERY
KillChainPhase.EXPLOITAITON
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_10
APT18
APT28
APT29
APT33
APT39
APT41
Akira
Axiom
Carbanak
Chimera
Cinnamon Tempest
Dragonfly
FIN10
FIN4
FIN5
FIN6
FIN7
FIN8
Fox Kitten
GALLIUM
Ke3chang
LAPSUS$
Lazarus Group
Leviathan
OilRig
POLONIUM
PittyTiger
Sandworm Team
Silence
Silent Librarian
Suckfly
Threat Group-3390
Wizard Spider
menuPass
APT29
APT32
FIN10
FIN7
HAFNIUM
Kimsuky
PROMETHIUM
Tropic Trooper
Turla

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

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

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Excessive user account lockouts for $user$ in a short period of time 36 60 60
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation ✅ Passing N/A N/A N/A
Unit ✅ Passing Dataset XmlWinEventLog:Security XmlWinEventLog
Integration ✅ Passing Dataset XmlWinEventLog:Security XmlWinEventLog

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: 6