The following analytic utilizes PowerShell Script Block Logging (EventCode=4104) to identify the execution of the
Get-NetUSer commandlets with specific parameters. These commandlets are part of PowerView, a PowerShell tool used to perform enumeration and discovery on Windows Active Directory networks. As the names suggest, these commandlets are used to identify domain users in a network and combining them with the
-SPN parameter allows adversaries to discover domain accounts associated with a Service Principal Name (SPN). Red Teams and adversaries alike may leverage PowerView and these commandlets to identify accounts that can be attacked with the Kerberoasting technique.
- Type: TTP
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2022-06-22
- Author: Gowthamaraj Rajendran, Splunk
- ID: a7093c28-796c-4ebb-9997-e2c18b870837
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 7 `powershell` EventCode=4104 (ScriptBlockText =*Get-NetUser* OR ScriptBlockText=*Get-DomainUser*) ScriptBlockText= *-SPN* | stats count min(_time) as firstTime max(_time) as lastTime by EventCode ScriptBlockText Computer UserID | rename Computer as dest | rename UserID as user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `windows_powerview_spn_discovery_filter`
The SPL above uses the following Macros:
windows_powerview_spn_discovery_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
The following analytic requires PowerShell operational logs to be imported. Modify the powershell macro as needed to match the sourcetype or add index. This analytic is specific to 4104, or PowerShell Script Block Logging.
Known False Positives
False positive may include Administrators using PowerView for troubleshooting and management.
Associated Analytic Story
|27.0||30||90||PowerView commandlets used for SPN discovery executed on $dest$|
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: 1