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Description

The following analytic detects potential account discovery activities using the 'net' command, commonly employed by malware like Trickbot for reconnaissance. It leverages Endpoint Detection and Response (EDR) data, focusing on specific command-line patterns and process relationships. This activity is significant as it often precedes further malicious actions, such as lateral movement or privilege escalation. If confirmed malicious, attackers could gain valuable information about user accounts, enabling them to escalate privileges or move laterally within the network, posing a significant security risk.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2024-08-15
  • Author: Teoderick Contreras, Splunk, TheLawsOfChaos, Github Community
  • ID: 339805ce-ac30-11eb-b87d-acde48001122

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1087.002 Domain Account Discovery
T1087 Account Discovery Discovery
Kill Chain Phase
  • Exploitation
NIST
  • DE.CM
CIS20
  • CIS 10
CVE 


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| tstats `security_content_summariesonly` values(Processes.process) as process values(Processes.parent_process) as parent_process values(Processes.process_id) as process_id count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_net` AND (Processes.process="* user *" OR  Processes.process="*config*" OR Processes.process="*view /all*") by  Processes.process_name Processes.dest Processes.user Processes.parent_process_name 
| where count >=4 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `account_discovery_with_net_app_filter`

Macros

The SPL above uses the following Macros:

:information_source: account_discovery_with_net_app_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
  • Processes.dest
  • Processes.user
  • Processes.parent_process_name
  • Processes.parent_process
  • Processes.original_file_name
  • Processes.process_name
  • Processes.process
  • Processes.process_id
  • Processes.parent_process_path
  • Processes.process_path
  • Processes.parent_process_id

How To Implement

The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Processes node of the Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.

Known False Positives

Admin or power user may used this series of command.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
5.0 10 50 Suspicious $process_name$ usage detected on endpoint $dest$ by user $user$.

: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: 6